# System Construction of Athlete Health Information Protection Based on Machine Learning Algorithm

Authors: Long Liu; Xiaodong Fan
Journal: BioMed Research International
 (2022)

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## Abstract

The exercise volume and exercise level can be quantitatively assessed by measuring and collecting athletes’ health and exercise data. The protection of athletes’ health information has lately become an important research topic due to a rise in sports activities. However, due to the nature of the data and the limits of protection models, protecting athlete health data is a complex undertaking. Machine learning and blockchain have caused worldwide technological innovation, and it is bound to bring deep modifications to the sports industry. The main purpose of blockchain is security, decentralization, traceability, and credibility of the athlete’s health data protection and gathering system. To progress and increase the sports industry and methodically assess the physical fitness of sportspersons’ health information, this study concentrates on the Machine Learning and Blockchain-based Athlete Health Information Protection System (MLB-AHIPS) proposed in the sports industry. The ML technique is utilized to clean and handle the information to comprehend the recognition and secure managing of the sportsperson’s fitness information. The system uses attribute-based access control, which permits dynamic and fine-grained access to athlete health data, and then stores the health data in the blockchain, which can be secured and tamper-proof by expressing the respective smart contracts. The simulation outcomes illustrate that the suggested MLB-AHIPS attains a high accuracy ratio of 97.8%, security ratio of 98.3%, an efficiency ratio of 97.1%, scalability ratio of 98.9%, and data access rate of 97.2% compared to other existing approach.


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## Body

## 1. Introduction

The intelligent sports health management system quantifies, gathers, and preserves diverse health data from various perspectives [1] and does a comprehensive dialectical analysis so that individuals may completely comprehend their health state [2]. As a complete and systematic approach to health management, smart sports health management [3] has attracted the public’s curiosity. Smart sports health management attempts to syndicate conventional medicine’s fundamental diagnostic tools [4] with new information technology to evaluate health status using a summary and classification of previous health management research [5]. A large amount of technologically-based biomechanical and physiological data [6] is combined with mathematical algorithms to define sports monitoring [7]. On the other side, algorithms based on mechanical assumptions about how athletes work cannot collect, measure, and effectively support athletes’ health and performance [8].Machine learning (ML) is a part of Artificial Intelligence (AI) that permits a machine to think like a human and make decisions without involving humans [9]. It is the method of making robots learn without being overtly programmed [10]. The basic purpose of ML is to develop computer software that can access and learn from athletic data [11, 12]. Combined with the Internet of Things, machine learning may uncover hidden patterns in massive amounts of athlete health data, enabling improved prediction and recommendation systems [13]. In healthcare, IoT and machine learning have been utilized to enable automated technologies to compile medical records [14], diagnose illnesses, and most significantly, monitor patients in real-time [15]. Different machine learning algorithms perform differently on different datasets. The utility and effectiveness of a machine learning solution, in general, are determined by the kind and characteristics of health data and the efficiency of the learning algorithms [16].Due to increased sports activities, the protection of athletes’ health information has recently become an important research topic. However, it is challenging to protect athlete health data because of the nature of the data and the limitations of protection models. Machine learning is the way to go when large athlete health data collection is necessary for analysis or pattern identification [17]. Athlete information such as health, performance, fitness, training, evaluation, and winning strategies has been protected. Blockchain technology guarantees the transparency, openness, and unforgeability of information in the sports industry. Distributed transactions and data management are features of the blockchain. Both referees and competition organizers are entitled to upload sportspersons’ performances. Blockchain technology can streamline data management and ease the pressure of information uploading and correcting for data center supervisors [18]. A tamper-proof record of sensitive activities may be created securely and quickly using blockchain technology. Teams may create new revenue sources and methods to interact with their supporters using blockchain in sports. The employment of private and public keys in blockchain transactions gives users full control over their data, enabling them to own it. It is illegal for third-party intermediaries to abuse or access data. Personal data saved on the blockchain can be accessed only by those who have the right to do so.The contributions of the proposed method are as follows:(i)
Constructing the machine learning and Blockchain-based Athlete Health Information Protection System (MLB-AHIPS) in the sports industry(ii)
Focusing on data sharing and athlete data privacy protection of smart healthcare, this study explores in-depth traditional encryption approaches, proxy re-encryption, attributed-based encryption, etc(iii)
The numerical outcomes have been executed, and the recommended model improves the accuracy, security, scalability, efficiency, and data access rate compared with other existing methodsThe rest of the study is structured as follows: sections1 and 2 deliberate the introduction and related analysis of athlete data protection systems. In section 3, the MLB-AHIPS have been suggested. In Section 4, the results and discussion have been performed. In Section 5, the conclusion and future scope have been deliberated.


## 2. Related Work

Xin et al. [18] suggested a Mobile Edge Computing Technology (MECT). This study is aimed at using mobile edge computing to collect real-time data from physical fitness tests, analyze it, and then transmit the results using machine learning technology. One of the key issues in physical education at the country’s top schools and institutions is developing sports management systems that concentrate on data collecting, organization, analysis, timeliness, and direction. As a result, a data-driven health management system and physical fitness have emerged, making the value of guiding and instruction increasingly difficult to accomplish.Rahaman et al. [19] proposed an IoT-based Smart Health Monitoring System (SHMS) to emphasize common design and implementation trends for intelligent IoT-based smart health monitoring data. The findings of the testing demonstrate that it can reduce data security concerns. The model is intended to utilize a Raspberry Pi as a Microcontroller Unit (MCU) and the Lo-Ra module for data spread and recognition of headaches, hearing problems, and rapid pulse rate, and utilizing an RFID tag for security and ZigBee for data transfer.Ahmid et al. [20] proposed an intelligent and secure Internet of Things method for healthcare systems to monitor patient heart rate, detect a critical condition before it occurs, and make quick and appropriate judgments in an emergency. Depending on the results of the experiments, the suggested system is suitable, dependable, and provides data security at a cheap cost.Yang and Chen [21] introduced a Web Database-based Sports Health Data Management System (WD-SHDMS). This study is aimed at creating a workable athletic health plan based on technical analysis and assessment to improve students’ physical quality while minimizing effort. The experimental findings in the research involved scientific analysis and evaluation of data to improve athletes’ health data while reducing workload.Cheng [22] presented a sports data gathering system based on the Internet of Things (IoT). This study showed a sports data gathering system based on the IoT that was expressly developed and developed for the healthcare industry, specifically in sports. Measures of professional medical equipment are compared to acquiring equipment in a practical situation. These findings backed the data gathering equipment’s accuracy and proved that the proposed system meets its design criteria.Feng and Chang [23] introduced the IoT system for spatial structure health monitoring with spatial structure health monitoring data characteristics. The numerical outcomes reveal that when great temperatures exist, the overall stress of the rods decreases to some extent. The basic design of an IoT system for monitoring the health of a building’s spatial structure was presented in this paper. An algorithm for data handling at the application layer was constructed using cloud computing, and cloud information for spatial structure observing was achieved.Sun [24] proposed an adaptive federated learning technique and a personalized federated learning algorithm based on DL to investigate the elements impacting pupil sports performance and make recommendations for development. It was resolved that the model provided in this study can precisely forecast the pupil’s athletic performance with the average accurateness ratio.Abhishek et al. [25] proposed a Modified Blowfish Algorithm to keep patient data safe and secure while stored on various platforms. The method encrypts files 72 percent of the time and decrypts them 48 percent. Hospitals around the country have medical records that may be used for comparison by professionals. It is still possible for hackers to obtain access to critical information, putting security, and privacy at risk.Zhao et al. [26] proposed a Posture Recognition Algorithm (PRA). This study uses the IoT and big data technology to improve the physical exercise restoration system’s data processing and combines it with big data processing technology to investigate the factors impacting exercise recovery and increase its effectiveness. The findings demonstrate that the strategy developed in this work positively affects physical practice recovery.Meng et al. [28] suggested a Sports Health Management Model based on Deep Learning (SHMM-DL). This article aimed to develop an SHMM-DL that will teach students how to actively participate in physical activity, consequently improving their physical fitness. It creates a plan for sports health management and likens and examines data. The experimental results include the development of a positive attitude toward health and increased health awareness, reversing the general deterioration in athletes’ physical fitness data.When a massive amount of athlete health data must be examined, the protection of the athlete health data becomes a concern. As a result, machine learning is the best approach to follow. Machine Learning and Blockchain-based Athlete Health Information Protection System (MLB-AHIPS) has been constructed to use artificial intelligence to predict athletes’ health information accurately and protect using Homomorphic encryption. The Machine Learning and Blockchain-based Athlete Health Information Protection System (MLB-AHIPS) results will help enhance the shielding methodology, athlete health information protection accuracy, and the variables that impact security.


## 3. Machine Learning and Blockchain-Based Athlete Health Information Protection System (MLB-AHIPS)

AI and machine learning in sports applications allow sports companies to leverage their data to better every aspect of their operations. A sports team may benefit from predictive analytics in every aspect of its operation, from player recruitment, and performance to ticket sales and marketing. In sports, Artificial Intelligence (AI) is being utilized to help athletes improve their performance and health. Sports people may avert major injury using wearables that monitor strain and tear levels. Machine learning can be applied to security in the sports industry, such as malware analysis, prediction, and clustering security events. It can identify previously unknown attacks with no recognized signature. Personal and sensitive data, such as phone numbers, IDs, and medical records, are often included in the athlete data used for machine learning training. This article aims to address the privacy problem in machine learning and how it may be attacked and then highlights the privacy protection techniques and characteristics in machine learning utilizing blockchain technology. Individual athletes and the team benefit from modern coaching’s utilization of big data. Using data science, coaches of professional sports teams, in particular, can construct hyper-personalized player matches and other plans for every match the team participates in. This study introduces blockchain technology into athletic data protection, management, and storage to decrease maintenance costs and guarantee data security. This study proposes that the MLB-AHIPS system has been constructed for sports health data protection in the sports industry.Figure1 shows the proposed MLB-AHIPS system. The decentralized ledger stores all information acquired by the athletes’ wearable sensors. These records are kept for the benefit of medical professionals, who may use them to see how the athlete’s condition has changed over time. As a result, the original data set is preserved, yet the raw data is transformed into numerical features extracted that may be processed. The machine learning model observes the attack detection in the data transmission network. Compared to applying machine learning directly to raw data, this method provides better results, athletes with a high risk of injury may be identified using the current ML model. A great deal of personal information is exchanged between many athletes in our use case. These medical records must be kept secret and only accessible by a few people inside our system to ensure athletes’ integrity because of attacks. Blockchain-based machine learning for electronic health records is at the heart of our architecture’s design. This paper incorporates an improved smart contract-based inference engine that leverages real-time fitness equipment and user profiles to infer new information in the proposed intelligent fitness blockchain platform. The security of the proposed athlete fitness system is examined in light of potential threats. The encryption utilized by the safe fitness system is based on an elliptic curve that is difficult for an attacker to calculate. To generate a private key by solving the elliptic curve technique, an intruder would need a lot of computing power. Blockchain can monitor, plan, analyze, and execute athlete health data. For each session agreement, each node takes the information of the private key. Finally, smart contracts store the athlete’s health and fitness records effectively. A smart contract is a program recorded on a blockchain that executes when a set of criteria is satisfied. When a contract is automated, both parties may be sure of what will happen without the need for an intermediary or a waste of time and resources.Figure 1
Proposed MLB-AHIPS system.Figure2 shows the athlete’s health information function. Athletic health data protection collects and analyses athletes’ vital statistics, such as their physical fitness levels and other relevant data. They use this information to provide personalized sports health fitness services, access, collect feedback from athletes, transfer information, protect the information, and then do the same information analysis as before. To conduct experimental research, people may use these criteria to watch athletes’ health and closely change their exercise levels.Figure 2
Athlete health information function.Figure3 shows the Machine learning and threats to athlete health data. Athlete raw data has been collected, and features are extracted. Furthermore, a feature vector is formulated for cleaning data. ML models have been utilized for predicting attacks such as re-construction attacks and model inversion attacks. The testing and training results are obtained. Secure channels may transfer private information between data owners and computation parties if they are not connected. To be sure, it would be stored in its unmodified state on the compute server(s), and it is not a guarantee. This is the most severe vulnerability since sensitive data is vulnerable to insider and outsider assaults. Personal data might be recorded as raw information or features derived from the raw information and stored in a database. Storing information in a raw state puts it at greater risk since it is ready for processing in any manner. Even when just the features (extracted from the raw information) are sent and kept to the computing party servers, re-construction attacks pose a hazard. Data records and class labels produce the attack model with the target model to produce an athlete’s data record in a training set.Figure 3
Machine Learning and threats in athlete health data.The adversary’s purpose is to use their feature vector knowledge to reassemble the secret raw data. Re-construction attack needs white-box access to the machine learning model, i.e., knowledge of the feature vectors inside the model. Such attacks are conceivable when the feature vectors used in the machine learning training phase are not detached after generating the intended ML model. K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) approaches store feature vectors in models. It is possible to successfully reconstruct an image of a fingerprint (raw data) using a minutiae template (features), and it is possible to successfully reconstruct a touch event (raw information) using gesture characteristics like direction and velocity on mobile devices. SVM and KNN are excellent examples of the trade-offs that may be made when using machine learning (ML). However, SVM can only recognize a small subset of patterns since it is less computationally intensive than KNN. It is possible to identify complicated patterns with the help of KNN, and its output is more difficult to decipher than with other methods.As a consequence of not securing private information in its feature form in both circumstances, authentication systems were put at risk, putting its users’ privacy at risk (since attackers may acquire access to the users’ devices). A machine learning system may be misled into thinking; the raw information belongs to a certain information owner, while another re-construction attack may disclose sensitive information, like the location or age of the data user. In contrast to model inversion attacks, membership inference attacks infer whether a sample was included in the training set based on model outcomes.


### 3.1. Preposition 1 (machine learning for athlete data gathering).

Most data can be categorized into four basic types from a machine learning perspective: categorical data, numerical data, text, and time-series data. Machine learning is a collection of technologies that excel at extracting insights and patterns from large data sets. Recall and precision rates are utilized as assessment indicators to reproduce the accuracy of the sport’s health information protection system. The recall of a machine learning model depends on positive samples and is unaffected by negative samples. Positive samples are as follows: all positive samples, whether rightly or mistakenly labeled, should be considered when calculating Precision’s value. The recall is concerned with categorizing all positive samples accurately. This model will get the information for the input data in this model. Accuracy defines the fraction of samples that be possessed by the typeDj amongst every sample that the model judges to be Dj; the recall ratio denotes the deliberation of the data and the fraction of samples judged to be right. In the binary classification issue, supposing that the positive sample sets output by the model is B, and the real positive sample dataset is A, the formulation for recall and precision are
(1a)P=B∩AB,(1b)R=B∩AA.As shown in equation ((1a) and (1b)), where the positive sample sets output by the model is B, and the real positive sample dataset is A for precision and recall. The accuracy ratio signifies the fraction of data samples that are properly categorized. Supposing that the overall number of samples in the data field is m, for samples j, the forecasted type labels are actual category labels; the accuracy ratio can be described as
(2)B=∑j=1mkxj=Xjm.As shown in equation (2), where kx denotes indicator functions. The higher the accuracy ratio, the closer indicated numbers, and the more precise the total sample forecasting.The data backhaul is primarily examined in the transmission model and data processing. The connection from the user to the computing server-side utilizes the millimeter-wave frequency band. A backhaul is a telecommunication phrase that refers to sending a signal from one location to another, most often a central location. Lines capable of transferring large amounts of data at high speeds are often known as backhauls. A backhaul is a telecommunication phrase that refers to sending a signal from one location to another, most often a central location. Lines capable of transferring large amounts of data at high speeds are often known as backhauls. Millimeter waves range from 30 to 300 gigahertz and have a wavelength range of 1 to 10 mm, making them an ideal carrier for transmitting data. They are known for their beam forming technique, low interference, high-frequency band, stable and reliable transmission, and the ability to penetrate solids like smoke, sand, dust, etc. As outside weather significantly impacts millimeter-wave communication, this study will not be addressing the fading issue specifically. There is no direct line of sight between the signal receiver and the signal transmitter when traveling through intermediate obstructions.DB expresses the path loss at a given location in this way:(3)Pathloss=PTransmission−PReceiver+HTransmission+HReceiver.As inferred from the equation (3), PTransmissionsignifies the overall transmit power, PReceiver symbolizes the overall received power, HTransmission denotes the transmit antenna, and HReceiver indicates receiver antenna gain. Propagation loss is used to describe the loss due to radio waves traveling across space at a constant speed. The transmitter and receiver characteristics do not affect the free space path loss (dB); hence, the transmission method has nothing to do with it.
(4)K¯lossH=32.5+20log10f+10βlog10g+B×g.As discussed in equation (4), where fMHz denotes carrier frequencies, and d signifies the path loss index, its value relies on the atmosphere in the transmission path. g symbolizes the transmission space among nodes, where the units are km; B indicates the attenuation coefficients of the environment. This study makes the transmission node on the backhaul connection aid every viewpoint in the simulation.In the feature-to-result mapping, a layer of sigmoid function mapping is auxiliary to limit the forecasted values to [0, 1], which can output the likelihoods of diverse types. The likelihoodqx=1y,θ specifies that the likelihood of x is 1 when the typical parameter y is provided and x=qx=1y,θ, and logistic regression models are determined.
(5)gθy=1+exp−θTy−1.As shown in equation (5), where θ=θ1,θ2,⋯θq denotes coefficient values respective to every feature, θ values. It can be determined by resolving the maximum probability estimation functions. Supposing that every sample in the dataset is independent of the others, the probability functions are
(6)Jθ=∏j=1mgθyxj∙1−gθy1−xj.As discussed in equation (6), where Jθ denotes probability functions, gθ indicates transmission distance, y represents number of samples. A case with a high probability value is selected as the final category for categorizing feature occurrences in real-world applications.
(7)x=fy=argmax∏i=1mQYi=yi/Y=dlQY=dl∑k=1LY=dlQYi=yi/Y=dl.As defined in equation (7), where x denotes feature occurrences. A solitary data stream is primarily examined here. Supposing that the data gathering end user’s demand is C, it primarily relied on the application and the user utilized. To reproduce the demand, it is presumed that the sensitivity and ambiguity of demand are contrariwise proportionate. This article uses the subsequent formulation to define the association between them
(8)C=b.q.1a.As shown in equation (8), where end user’s demand is C.


### 3.2. Preposition 2 (blockchain technology for data security).

Public and private keys or an encryption method and an encryption key are used to encrypt athlete health data written on the blockchain. As a result, anybody who does not have the secret key cannot decipher what is written on the blockchain’s public ledger.Figure4 shows the blockchain technology. The sender’s private key signs each transaction. The security of the transaction is assured based on the signature. Thus, any alteration of these transactions throughout transmission can be evaded. Blocks are a blockchain records that consists of confirmed transactions. Therefore, every exposed transaction can be auxiliary to a block. Eventually, for a fresh block comprising transactions to be auxiliary to the blockchain, it should be validated by a designated person termed a minor. This validation process is termed manage. Every block in the blockchain is connected to the prior blocks. This connection is prepared by implanting the hash particular to the prior blocks. Data integrity is ensured by using hash functions, which are often paired with digital signatures. A 1-bit change in a message will result in a different hash when using a suitable hash function (on average, half of the bits change). A message is first hashed, and then the hash is signed using digital signatures.Figure 4
Blockchain illustration.Stage 1. Healthcare centers A and B request cloud service providers to produce their private and public keys. After the provider distinctly produces a pair of private keys and public keys for Healthcare centers A and B, cloud service providers will return to Healthcare centers A and B.Stage 2. Arbitrarily select two moderately large and independent prime numbersu, v, to create
(9)uv,u−1v−1=1.As inferred from the equation (9), where u,v is prime numbers.Stage 3. Compute(10)m=uvφm=u−1v−1,emakesGcde,φm=1,c=nemodm,λ=cmu−1,v−1.As shown in equation (10), where n is the data to be encrypted.Stage 4. Select random integersh to create h∈Zm∗.Stage 5. Utilize expression (10) below and compute the existence of modularized multiplicative inverse to identify m and distinct the series of h.
(11)μ=Kcλmodm2−1modm.As shown in equation (11), where function K is described as the following expression:
(12)Kω=ω−1m.Stage 6. The public key ism,h, and the private key is λ,μ.Stage 7. Produce encrypted files.Healthcare center A first utilizes Rivest–Shamir–Adleman (RSA) encryption to compute and encrypt information to produce the encrypted file C1. Then, Healthcare center A encrypts in line with the second layer, encrypts the keys of RSA with its public key, and produces the encrypted file C2. In conclusion, Healthcare center A uploads two encrypted files: C1 and C2, into servers.(13)Encrypt⟶Encn,ul,(14)c=hn∙mrmodm2.Stage 8. Maken the data is encrypted and n∈ZmStage 9. Randomly chooser and make r∈Zm∗Stage 10. Compute ciphertextStage 11. Create the key of proxy re-encryption.Healthcare center A requests the public key of Healthcare center A from cloud service providers. The provider can return these public keys to Healthcare center A. Healthcare center A uses the public key of Healthcare center B and its private key to produce the re-encryption key, and then Healthcare center A uploads the afresh produced re-encryption key into the server.Stage 12. Compute public keys and private keys (RpK, RsK)Stage 13. The Paillier algorithm produces the re-encrypted ciphertexts, and public key (Rpk) are sent to the cloud computing services.For the provided public key (Rpk) and the subsequent-layer ciphertexts, this model can utilize the re-encryption keys and produce the initial-layer ciphertext of public key (Rpk). The server utilizes re-encryption keys and ciphertexts uploaded by Healthcare center A deportment proxy re-encryption calculating and makes novel ciphertexts.Stage 14. Healthcare center B requests information anddecrypts⟶Decc,wlHealthcare center B requested the cloud servers to decrypt the information and the respective ciphertexts. The cloud server sends the re-encrypted text to Healthcare center B. Healthcare center B decrypts ciphertexts, acquires keys, and utilizes RSA for decryption to acquire the actual plaintext information.Stage 15. Ciphertextsc∈Zm∗.Stage 16. Calculate the data.(15)n=Kcλmodm2Lhλmodm2modm.The extent of the capability of this system against malicious attacks, initially, create the client set to establish encryption tasksσT. For the jth responder in σT, its encrypted information is n-dimensional vectors, signified by wj. So, wj=wj,i,i=1,⋯n. After receiving the encrypted information, cloud nodes ENT can request σT the trustworthiness of each client in the response side to central verification center. The dependability extent wj is weighed through the variance among wj and the final result wT. Here, this study has described the extent of the variance among wj and wT as the square of Euclidean distance among wj and wT, which can be computed via the subsequent expression as
(16)dj=∑i=1nwj,i−wT,i2.As shown in equation (16), the lesser values of dj, the greater the trustworthiness of wj. The degree of variance is the degree of dissimilarity among the parameter values, and it is utilized to measure the safety hazard.Figure5 shows the athlete’s health data and smart contract configuration. Wearable devices should be available to every athlete, allowing them to keep checks on various health-related data points that help determine their current condition (like calories, heart rate, steps, distance, and temperature). The data on this device is synchronized with the mobile app via Bluetooth. The mobile application reads the wearable device’s health data and stores it in the athlete’s smart contract. Depending on the patient’s setup, the data upload to the blockchain may be done on-demand or as a task that runs each day for 2 epochs. A wearable device keeps tabs on each patient in our design. Athletes’ smart contracts need devices like this to collect data. A single smart contract is used for each athlete.Figure 5
Athlete’s health data and smart contract configuration.The transaction proposal (transaction request) is submitted to the network’s validating peers (miners) to accept the transaction and add value to the smart contract based on the wearable device’s data. A legitimate or invalid transaction is based on a consensus protocol determined by verifying peers. Valid transactions are added to the new block by the other peers who sign them. The modern health data input is recorded in the smart contract, and the transaction’s success is reported to the mobile application after the transaction is confirmed. The suggested MLB-AHIPS system using blockchain technology achieves a high accuracy ratio, security ratio, data access rate, efficiency ratio, and scalability ratio compared with other existing methods.


## 3.1. Preposition 1 (machine learning for athlete data gathering).

Most data can be categorized into four basic types from a machine learning perspective: categorical data, numerical data, text, and time-series data. Machine learning is a collection of technologies that excel at extracting insights and patterns from large data sets. Recall and precision rates are utilized as assessment indicators to reproduce the accuracy of the sport’s health information protection system. The recall of a machine learning model depends on positive samples and is unaffected by negative samples. Positive samples are as follows: all positive samples, whether rightly or mistakenly labeled, should be considered when calculating Precision’s value. The recall is concerned with categorizing all positive samples accurately. This model will get the information for the input data in this model. Accuracy defines the fraction of samples that be possessed by the typeDj amongst every sample that the model judges to be Dj; the recall ratio denotes the deliberation of the data and the fraction of samples judged to be right. In the binary classification issue, supposing that the positive sample sets output by the model is B, and the real positive sample dataset is A, the formulation for recall and precision are
(1a)P=B∩AB,(1b)R=B∩AA.As shown in equation ((1a) and (1b)), where the positive sample sets output by the model is B, and the real positive sample dataset is A for precision and recall. The accuracy ratio signifies the fraction of data samples that are properly categorized. Supposing that the overall number of samples in the data field is m, for samples j, the forecasted type labels are actual category labels; the accuracy ratio can be described as
(2)B=∑j=1mkxj=Xjm.As shown in equation (2), where kx denotes indicator functions. The higher the accuracy ratio, the closer indicated numbers, and the more precise the total sample forecasting.The data backhaul is primarily examined in the transmission model and data processing. The connection from the user to the computing server-side utilizes the millimeter-wave frequency band. A backhaul is a telecommunication phrase that refers to sending a signal from one location to another, most often a central location. Lines capable of transferring large amounts of data at high speeds are often known as backhauls. A backhaul is a telecommunication phrase that refers to sending a signal from one location to another, most often a central location. Lines capable of transferring large amounts of data at high speeds are often known as backhauls. Millimeter waves range from 30 to 300 gigahertz and have a wavelength range of 1 to 10 mm, making them an ideal carrier for transmitting data. They are known for their beam forming technique, low interference, high-frequency band, stable and reliable transmission, and the ability to penetrate solids like smoke, sand, dust, etc. As outside weather significantly impacts millimeter-wave communication, this study will not be addressing the fading issue specifically. There is no direct line of sight between the signal receiver and the signal transmitter when traveling through intermediate obstructions.DB expresses the path loss at a given location in this way:(3)Pathloss=PTransmission−PReceiver+HTransmission+HReceiver.As inferred from the equation (3), PTransmissionsignifies the overall transmit power, PReceiver symbolizes the overall received power, HTransmission denotes the transmit antenna, and HReceiver indicates receiver antenna gain. Propagation loss is used to describe the loss due to radio waves traveling across space at a constant speed. The transmitter and receiver characteristics do not affect the free space path loss (dB); hence, the transmission method has nothing to do with it.
(4)K¯lossH=32.5+20log10f+10βlog10g+B×g.As discussed in equation (4), where fMHz denotes carrier frequencies, and d signifies the path loss index, its value relies on the atmosphere in the transmission path. g symbolizes the transmission space among nodes, where the units are km; B indicates the attenuation coefficients of the environment. This study makes the transmission node on the backhaul connection aid every viewpoint in the simulation.In the feature-to-result mapping, a layer of sigmoid function mapping is auxiliary to limit the forecasted values to [0, 1], which can output the likelihoods of diverse types. The likelihoodqx=1y,θ specifies that the likelihood of x is 1 when the typical parameter y is provided and x=qx=1y,θ, and logistic regression models are determined.
(5)gθy=1+exp−θTy−1.As shown in equation (5), where θ=θ1,θ2,⋯θq denotes coefficient values respective to every feature, θ values. It can be determined by resolving the maximum probability estimation functions. Supposing that every sample in the dataset is independent of the others, the probability functions are
(6)Jθ=∏j=1mgθyxj∙1−gθy1−xj.As discussed in equation (6), where Jθ denotes probability functions, gθ indicates transmission distance, y represents number of samples. A case with a high probability value is selected as the final category for categorizing feature occurrences in real-world applications.
(7)x=fy=argmax∏i=1mQYi=yi/Y=dlQY=dl∑k=1LY=dlQYi=yi/Y=dl.As defined in equation (7), where x denotes feature occurrences. A solitary data stream is primarily examined here. Supposing that the data gathering end user’s demand is C, it primarily relied on the application and the user utilized. To reproduce the demand, it is presumed that the sensitivity and ambiguity of demand are contrariwise proportionate. This article uses the subsequent formulation to define the association between them
(8)C=b.q.1a.As shown in equation (8), where end user’s demand is C.


## 3.2. Preposition 2 (blockchain technology for data security).

Public and private keys or an encryption method and an encryption key are used to encrypt athlete health data written on the blockchain. As a result, anybody who does not have the secret key cannot decipher what is written on the blockchain’s public ledger.Figure4 shows the blockchain technology. The sender’s private key signs each transaction. The security of the transaction is assured based on the signature. Thus, any alteration of these transactions throughout transmission can be evaded. Blocks are a blockchain records that consists of confirmed transactions. Therefore, every exposed transaction can be auxiliary to a block. Eventually, for a fresh block comprising transactions to be auxiliary to the blockchain, it should be validated by a designated person termed a minor. This validation process is termed manage. Every block in the blockchain is connected to the prior blocks. This connection is prepared by implanting the hash particular to the prior blocks. Data integrity is ensured by using hash functions, which are often paired with digital signatures. A 1-bit change in a message will result in a different hash when using a suitable hash function (on average, half of the bits change). A message is first hashed, and then the hash is signed using digital signatures.Figure 4
Blockchain illustration.Stage 1. Healthcare centers A and B request cloud service providers to produce their private and public keys. After the provider distinctly produces a pair of private keys and public keys for Healthcare centers A and B, cloud service providers will return to Healthcare centers A and B.Stage 2. Arbitrarily select two moderately large and independent prime numbersu, v, to create
(9)uv,u−1v−1=1.As inferred from the equation (9), where u,v is prime numbers.Stage 3. Compute(10)m=uvφm=u−1v−1,emakesGcde,φm=1,c=nemodm,λ=cmu−1,v−1.As shown in equation (10), where n is the data to be encrypted.Stage 4. Select random integersh to create h∈Zm∗.Stage 5. Utilize expression (10) below and compute the existence of modularized multiplicative inverse to identify m and distinct the series of h.
(11)μ=Kcλmodm2−1modm.As shown in equation (11), where function K is described as the following expression:
(12)Kω=ω−1m.Stage 6. The public key ism,h, and the private key is λ,μ.Stage 7. Produce encrypted files.Healthcare center A first utilizes Rivest–Shamir–Adleman (RSA) encryption to compute and encrypt information to produce the encrypted file C1. Then, Healthcare center A encrypts in line with the second layer, encrypts the keys of RSA with its public key, and produces the encrypted file C2. In conclusion, Healthcare center A uploads two encrypted files: C1 and C2, into servers.(13)Encrypt⟶Encn,ul,(14)c=hn∙mrmodm2.Stage 8. Maken the data is encrypted and n∈ZmStage 9. Randomly chooser and make r∈Zm∗Stage 10. Compute ciphertextStage 11. Create the key of proxy re-encryption.Healthcare center A requests the public key of Healthcare center A from cloud service providers. The provider can return these public keys to Healthcare center A. Healthcare center A uses the public key of Healthcare center B and its private key to produce the re-encryption key, and then Healthcare center A uploads the afresh produced re-encryption key into the server.Stage 12. Compute public keys and private keys (RpK, RsK)Stage 13. The Paillier algorithm produces the re-encrypted ciphertexts, and public key (Rpk) are sent to the cloud computing services.For the provided public key (Rpk) and the subsequent-layer ciphertexts, this model can utilize the re-encryption keys and produce the initial-layer ciphertext of public key (Rpk). The server utilizes re-encryption keys and ciphertexts uploaded by Healthcare center A deportment proxy re-encryption calculating and makes novel ciphertexts.Stage 14. Healthcare center B requests information anddecrypts⟶Decc,wlHealthcare center B requested the cloud servers to decrypt the information and the respective ciphertexts. The cloud server sends the re-encrypted text to Healthcare center B. Healthcare center B decrypts ciphertexts, acquires keys, and utilizes RSA for decryption to acquire the actual plaintext information.Stage 15. Ciphertextsc∈Zm∗.Stage 16. Calculate the data.(15)n=Kcλmodm2Lhλmodm2modm.The extent of the capability of this system against malicious attacks, initially, create the client set to establish encryption tasksσT. For the jth responder in σT, its encrypted information is n-dimensional vectors, signified by wj. So, wj=wj,i,i=1,⋯n. After receiving the encrypted information, cloud nodes ENT can request σT the trustworthiness of each client in the response side to central verification center. The dependability extent wj is weighed through the variance among wj and the final result wT. Here, this study has described the extent of the variance among wj and wT as the square of Euclidean distance among wj and wT, which can be computed via the subsequent expression as
(16)dj=∑i=1nwj,i−wT,i2.As shown in equation (16), the lesser values of dj, the greater the trustworthiness of wj. The degree of variance is the degree of dissimilarity among the parameter values, and it is utilized to measure the safety hazard.Figure5 shows the athlete’s health data and smart contract configuration. Wearable devices should be available to every athlete, allowing them to keep checks on various health-related data points that help determine their current condition (like calories, heart rate, steps, distance, and temperature). The data on this device is synchronized with the mobile app via Bluetooth. The mobile application reads the wearable device’s health data and stores it in the athlete’s smart contract. Depending on the patient’s setup, the data upload to the blockchain may be done on-demand or as a task that runs each day for 2 epochs. A wearable device keeps tabs on each patient in our design. Athletes’ smart contracts need devices like this to collect data. A single smart contract is used for each athlete.Figure 5
Athlete’s health data and smart contract configuration.The transaction proposal (transaction request) is submitted to the network’s validating peers (miners) to accept the transaction and add value to the smart contract based on the wearable device’s data. A legitimate or invalid transaction is based on a consensus protocol determined by verifying peers. Valid transactions are added to the new block by the other peers who sign them. The modern health data input is recorded in the smart contract, and the transaction’s success is reported to the mobile application after the transaction is confirmed. The suggested MLB-AHIPS system using blockchain technology achieves a high accuracy ratio, security ratio, data access rate, efficiency ratio, and scalability ratio compared with other existing methods.


## 4. Results and Discussion

This study suggested the MLB-AHIPS system using blockchain technology for athlete health data protection. 100 athletes were selected to analyze the accuracy ratio, security ratio, data access rate, efficiency ratio, and scalability ratio. This study utilized thehttps://libguides.und.edu/kinesiology/data-sets [27] for athlete health data protection. The Equity requires coeducational postsecondary Institutions with Athletics Disclosure Act (EADA) to submit annual sports data via a web-based data gathered for all institutions that receive Title IV funding (i.e., federal learner aid program) and have an intercollege sports program to this database. Visitors to the website may examine or download information about specific institutions and aggregate statistics. Students, coaching staff and wages, team income and costs, and associated extra information, if necessary, are all included in the report.
(i)
Accuracy ratioMachine learning and blockchain-based solutions for the security of sports and athlete health data are examined in this article. Data backhaul and accuracy indexes show how a blockchain’s capabilities might be used for data gathering systems. This article analyzes the medical data collecting system’s demand, accuracy and recall rates, and data access rate. This research indicated that collecting sports medical data is advantageous to the training of sportspersons and the growth of the sports sector to satisfy the rising demands of sportsperson health. This paper assumes the accuracy indices as shown in equations (1a) and (1b), (2)) and the formulation of information return and conducts accuracy demand study and recall analysis of the athlete medical data gathering system. The Accuracy Rate measures the proportion of correctly predicted values in a dataset. In this research, the number of right predictions is divided by how many samples were used to make those predictions. Figure 6 demonstrates the accuracy ratio.
(ii)
Security ratioFigure 6
Accuracy ratio.As a prerequisite for objective and precise athlete performance assessment, ensuring the authenticity and reliability of every piece of included sportsperson training or test information is becoming a critical and demanding problem requiring extensive research. In this article, blockchain technology has been used to ensure the dependability and validity of sportsperson information that is likely integrated, shared, and sent across several parties. A new approach for reliably predicting athlete performance is based on time-aware training or test information created in the past and secured by machine learning and blockchain technology. The security ratio has been predicted based on equation (10). Figure 7 denotes the security ratio.
(iii)
Efficiency ratioFigure 7
Security ratio.Cryptographic protocols could perform machine learning testing/training on encrypted information for sports health information protection when a specific machine learning application needs data from multiple input parties. To improve efficiency, several of these solutions require data owners to submit their encrypted information to the computing servers, which reduces the difficulty of secure two- or three-party calculations. Along with being more efficient, these methods do not need the input parties to stay online in sports. The efficiency of data is one of the most enticing benefits of blockchain technology, which creates a transaction log that is both auditable and valid. Anyone may join the network and see all its data, which is the whole point of blockchain as a transparency mechanism. From equation (12), the efficiency ratio has been calculated. The proposed MLB-AHIPS achieves a high-efficiency ratio. Figure 8 illustrates the efficiency ratio.
(iv)
Scalability ratioFigure 8
Efficiency ratio.The scalability of blockchain networks is the ability of sports platforms to support an increasing load of transactions of athlete health data and increase the number of nodes in the network. Limitations, transaction fees, block size, and reaction time impact the blockchain’s scalability. Massive-scale internet-scale analysis of large volumes of athlete health data is made possible using a flexible, frequently nonparametric, scalable statistics systems, machine learning, and data mining approaches. Scaling in the setting of blockchains denotes increasing the system’s throughput, as measured by transactions per second. Layer 1 solutions enhance the blockchain network’s core features and traits, like increasing the block size limit or decreasing the block verification time. Equation (14) shows the scalability ratio effectively. Figure 9 shows the scalability ratio.
(v)
Data access ratioFigure 9
Scalability ratio.Access to the blocks is granted to all resources linked to the blockchain. Consensus algorithms, like Proof of Stake (PoS), Proof of Work (PoW), and so on, are used to verify the blocks. Only one authentication is required to access the services offered. Randomly chosen miners validate transactions in the Proof of Stake (POS) system. Blockchain transactions and new blocks are added to the network through a process known as Proof of Work (POW). The sports sector hopes to enhance data security and openness by creating this app. The strategy is doable due to the system implementing an access process to determine authentication. The use of machine learning and blockchain technology ensures that account information can only be accessed by those to whom the data owners have granted permission. Since the very beginning, blockchain has been nothing more than a public ledger used to record and preserve information about transactions. To utilize blockchain, users must have access to their private keys. Users do not require usernames and passwords to keep their sensitive information online. In addition, blockchain technology has long been regarded as an excellent method of storing sensitive information. Equation (15) signifies the data access ratio. Figure 10 signifies the data access ratio.Figure 10
Data access ratio.The suggested MLB-AHIPS system achieves high accuracy, security, scalability, efficiency, and data access rate compared to other existing Mobile Edge Computing Technology (MECT), Web Database-based Sports Health Data Management System (WD-SHDMS), Sports Health Management Model based on Deep Learning (SHMM-DL), Posture Recognition Algorithm (PRA) methods.


## 5. Conclusion

This study discussed the MLB-AHIPS system for secure data transmission in the sports industry using blockchain technology and understanding systems constructing the benefits and risks of using ML-based analysis of encrypted athlete medical data. Large-scale data training is an essential part of machine learning advancement. Cryptographic technologies create a new decentralized distributed database known as the blockchain. According to this research, blockchain-based machine learning may be used to secure athletes’ medical data privacy and security by combining two encryption methods: proxy re-encryption and attribute-based encryption. This research introduces a new and highly customizable privacy-preserving sports data fusion system. Privacy in synthetic databases may be protected using this strategy. A differential privacy system that is both flexible and adaptable is used to keep the data safe. Furthermore, this study investigates how to employ blockchain and machine learning technology in smart health scenarios. The numerical outcomes signify that the proposed MLB-AHIPS attains a high accuracy ratio of 97.8%, security ratio of 98.3%, an efficiency ratio of 97.1%, scalability ratio of 98.9%, and data access rate of 97.2% compared to other existing methods. Overall, this paper is fairly widespread; because of its limited space, the study on following-up must deliberate on achieving an effective assessment of sport-data protection with improved neural touch in time sequence data function testing.



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