# A Novel Approach for Fraud Detection in Blockchain-Based Healthcare Networks Using Machine Learning
###### Abstract  
Recently, the advent of blockchain (BC) has sparked a digital revolution in different fields, such as finance, healthcare, and supply chain. It is used by smart healthcare systems to provide transparency and control for personal medical records. However, BC and healthcare integration still face many challenges, such as storing patient data and privacy and security issues. In the context of security, new attacks target different parts of the BC network, such as nodes, consensus algorithms, Smart Contracts (SC), and wallets. Fraudulent data insertion can have serious consequences on the integrity and reliability of the BC, as it can compromise the trustworthiness of the information stored on it and lead to incorrect or misleading transactions. Detecting and preventing fraudulent data insertion is crucial for maintaining the credibility of the BC as a secure and transparent system for recording and verifying transactions. SCs control the transfer of assets, which is why they may be subject to several adverbial attacks. Therefore, many efforts have been proposed to detect vulnerabilities and attacks in the SCs, such as utilizing programming tools. However, their proposals are inadequate against the newly emerging vulnerabilities and attacks. Artificial Intelligence technology is robust in analyzing and detecting new attacks in every part of the BC network. Therefore, this article proposes a system architecture for detecting fraudulent transactions and attacks in the BC network based on Machine Learning (ML). It is composed of two stages: (1) Using ML to check medical data from sensors and block abnormal data from entering the blockchain network. (2) Using the same ML to check transactions in the blockchain, storing normal transactions, and marking abnormal ones as novel attacks in the attacks database. To build our system, we utilized two datasets and six machine learning algorithms (Logistic Regression, Decision Tree, KNN, Naive Bayes, SVM, and Random Forest). The results demonstrate that the Random Forest algorithm outperformed others by achieving the highest accuracy, execution time, and scalability. Thereby, it was considered the best solution among the rest of the algorithms for tackling the research problem. Moreover, the security analysis of the proposed system proves its robustness against several attacks which threaten the functioning of the blockchain-based healthcare application. healthcare; blockchain; SC; machine learning; security +
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0000-0001-5000-0001-5000][PERSON] 1,0000-0002-0002-0003-0003-0003-0003-0003-0003-0003-0003-003-0003-  
treatment, and prevention of diseases. Therefore, patients will benefit from a better quality of services in the health field [2; 3]. Despite the benefits of smart healthcare, it also has many challenges that need viable solutions. These challenges include quality of service (QoS), standard protocol support, delay and bandwidth-limited, and security and privacy. Smart healthcare system devices are vulnerable to many attacks, such as spoofing, cloud polling, RF jamming, and Denial of service (DoS). Additionally, transmitted data pass through highly heterogeneous networks and are frequently managed by third parties. These issues can also affect healthcare systems and patient safety [4]. As electronic health records contain sensitive and important information, it is susceptible to various security risks. If this data is compromised, it could have serious consequences for patient safety. For example, attackers could use this data to impersonate patients, gain access to their medical records, or even tamper with their treatment, thereby leading to potential medical accidents [5]. In 2017, hackers stole the personal data of over 15 million patients from Anthem, a large health insurance company. This data included names, addresses, social security numbers, and medical information. The attack was a major breach of privacy with serious consequences for the affected patients.