# Correlation Analysis between Sports and Antiaging Based on Medical Big Data

Authors: Ying Yang
Journal: Journal of Sensors
 (2022)

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## Abstract

With the aging of the population in China and even in the world becoming more and more serious, China has become the country with the largest proportion of the elderly population, and a series of social problems such as health and medical care brought about by aging are being actively responded by policies. Aging has also become a natural law that human beings cannot break free from. Although exercise cannot reverse the aging process, it can weaken the adverse effects caused by aging. Having good physical quality, keeping a happy mood, comfortable living environment, and friendly social relations are the secret recipe for prolonging life and resisting cell aging. On the contrary, if physical and mental exhaustion, various chronic diseases, negative life events, bad living environment, and social relations will seriously affect human life and quality of life. Sports promote the metabolism of the whole body. Exercise can stabilize blood sugar, avoid cardiovascular diseases, and even get a good mood. At the cellular level, exercise facilitates the transmission and absorption of nutrients, thus making tissues healthier to cope with the stress of daily life. According to the statistics of loving sports and not loving sports through medical big data, the physical fitness and cell health of the elderly who love exercise every day are better than those who do not love sports. Sports are controlled by the central nervous system, and there is a correlation between changes in motor ability and cognitive impairment. Aging is accompanied by the deterioration of skeletal muscle quality and strength, which is mainly due to the rapid degradation and slow synthesis of protein in skeletal muscle. Therefore, exercise is one of the most effective ways to delay the aging of muscles and bones. According to the great potential value of medical big data, this paper analyzes and explains the correlation between sports and antiaging.


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## Body

## 1. Introduction

At present, the sharing level of medical big data in China has not reached a higher level, which provides reference and basis for the correlation analysis between sports and antiaging, hoping to promote the transformation of medical care, medical insurance, medicine, medical research, and medical policy decision-making and help the development of related medical industries. Literature [1] tells the health problems of the elderly, and the community should set up corresponding fitness service facilities. Literature [2] tells us that sitting for a long time will accelerate the aging speed, and only exercise can maintain a healthy and youthful form. Literature [3] expounds the research and analysis of taking traditional Chinese medicine to delay aging. Literature [4] expounds the application scenarios of intelligent decision-making service of medical information in different fields. Literature [5] states that intelligent neurosurgery has gradually entered the stage of orbital, systematic, and large-scale development. Many R&D sections, such as big data mining, machine learning/deep learning/neural network, clinical decision support system/expert system, surgical navigation, and robot, have initially matured. Related applications have covered clinical diagnosis, treatment decision-making, surgical assistance, prognosis evaluation, simulated teaching, and other scenarios of various diseases in neurosurgery, but the whole is still in its infancy. Literature [6] studies the analysis and sharing of big data in precision medicine to provide a stable data base for medical development. Literature [7] analyzes that medical big data and artificial intelligence (AI) have great potential in improving the utilization rate of medical resources and service quality, but they also bring challenges in privacy protection and technical risks. Literature [8] studies and analyzes the accurate application of big data in cancer diagnosis and treatment, clinical medication guidance, chronic disease prevention and control, and other fields. Literature [9] shows the construction and development of medical science in the era of big data. Literature [10] expounds that artificial intelligence technology integrates big data and cloud computing in the medical industry to effectively promote the intelligent development of medical diagnosis and treatment. Literature [11] expounds the data management of hospital clinical by big data and the construction of medical engineering in literature [12]. Literature [13] expresses the application exploration of medical diagnosis under the background of big data. Literature [14] looks forward to the help of big data to the development of world medicine. Literature [15] says that intelligent medicine will eventually change the future of medicine. Literature [16] puts forward thoughts on the problems of artificial intelligence, literature [17] points out the application research of medical big data in tumors, literature [18–20] talks about the future development of artificial intelligence in the medical field, literature [21, 22] expounds the application value of medicine under the background of big data, and literature [23] applies big data to evaluate health.


## 2. Sports Situation of the Elderly at Home and Abroad

### 2.1. Sports Situation of the Elderly Abroad

After big data [24], Australia is rich in fitness activities for the elderly, among which the middle-aged and elderly sports meeting is one of the large-scale sports activities active in the middle-aged and elderly people in Australia. The development of community sports activities in the United States is mainly funded by government sponsorship or collection of membership fees. Most of the residents of the United States carry out very rich sports and physical fitness activities, which cover a wide range of exercise projects, and the quality of their activities is quite high. Japan is already the country with the highest proportion of the elderly population in the world. The elderly in Japan have relatively more leisure time, pay more attention to their physical and mental health, and tend to be able to carry out various sports and health care activities independently. In the choice of sports, they hope that the sports rules are easy to understand, the technology has certain technical content, and it is easy to learn to experience the fun of sports.


### 2.2. Sports Situation of the Elderly in China

With the development of the times, there are various forms of sports, but according to statistics [25], the sports choices of the elderly in China are relatively single, among which walking is the most favored by the elderly. Secondly, running, square dancing, cycling, and other activities have a wide audience. These events do not need too many skills or special sports equipment. They are the cheapest and simplest events with relatively small difficulty coefficient. Some common sports equipment in the home, such as table tennis, badminton, swimming, skipping rope, kicking shuttlecock, and other activities, are also within the scope of activities of the elderly, and a small number of elderly people with strong physical fitness will participate in some professional activities, such as mountaineering, basketball, diabolo shaking, softball, folk dance, martial arts, and health qigong (See Table 1 for details).Table 1
Statistical table of sports project names for the elderly in community.
Sports eventsNumber of peoplePercentage (%)Walk54029.7Running32017.6Square dance33418.4Table tennis21011.5Skipping rope18410.1Martial arts1457.9Mountaineering824.5


## 2.1. Sports Situation of the Elderly Abroad

After big data [24], Australia is rich in fitness activities for the elderly, among which the middle-aged and elderly sports meeting is one of the large-scale sports activities active in the middle-aged and elderly people in Australia. The development of community sports activities in the United States is mainly funded by government sponsorship or collection of membership fees. Most of the residents of the United States carry out very rich sports and physical fitness activities, which cover a wide range of exercise projects, and the quality of their activities is quite high. Japan is already the country with the highest proportion of the elderly population in the world. The elderly in Japan have relatively more leisure time, pay more attention to their physical and mental health, and tend to be able to carry out various sports and health care activities independently. In the choice of sports, they hope that the sports rules are easy to understand, the technology has certain technical content, and it is easy to learn to experience the fun of sports.


## 2.2. Sports Situation of the Elderly in China

With the development of the times, there are various forms of sports, but according to statistics [25], the sports choices of the elderly in China are relatively single, among which walking is the most favored by the elderly. Secondly, running, square dancing, cycling, and other activities have a wide audience. These events do not need too many skills or special sports equipment. They are the cheapest and simplest events with relatively small difficulty coefficient. Some common sports equipment in the home, such as table tennis, badminton, swimming, skipping rope, kicking shuttlecock, and other activities, are also within the scope of activities of the elderly, and a small number of elderly people with strong physical fitness will participate in some professional activities, such as mountaineering, basketball, diabolo shaking, softball, folk dance, martial arts, and health qigong (See Table 1 for details).Table 1
Statistical table of sports project names for the elderly in community.
Sports eventsNumber of peoplePercentage (%)Walk54029.7Running32017.6Square dance33418.4Table tennis21011.5Skipping rope18410.1Martial arts1457.9Mountaineering824.5


## 3. Sports Statistical Algorithm Model

### 3.1. Logistic Regression Model

#### 3.1.1. General Linear Regression Model

In statistical analysis with less fluctuation, the lever value of general linear model is defined as shown in the formula:(1)hii=XXTX−1Xi,where hii is the i-th diagonal element of the matrix H=XXTX−1Xi.The target value is estimated by the least squares of the general linear model, and the predicted valuey^ican be written as follows:
(2)y^i=∑j=1nhijyj=hiiyi+∑j≠inhijyj.y^takes the partial derivative of yi, as follows:
(3)∂y^i∂yi=hii,i=1,2,⋯,n.The subsampling algorithm of general linear model is an important subsampling strategy based on normalized empirical statistical lever score of input matrixX. The calculation of sampling probability distribution is as follows:
(4)πIleverage=hii∑i=1nhii,i=1,2,..n.


#### 3.1.2. Sensitivity to Misclassification

The formula for the logistic regression model is defined as follows:(5)Pyi=1∣xi=piβ=expxiTβ1+expxiTβ.When a certaini is selected, it is assumed that the tested event yiis misclassified symmetrically with the probability qi and that all other observations are correctly classified, that is, the probability of misclassification of these observations is qi =0, j≠i. If the sensitivity to error classification is calculated in the presence of error classification, the probability distribution of response variables is recorded as the following formula:
(6)Pyi=1ui=0=Pyi=0∣ui=1=qi.The marginal probability of observed eventyi=1 is obtained as follows:
(7)Pyi=1=Pyi=1∣ui=0+Pyi=1∣ui=1=1−qjexpxjTβ+qj1+expxjTβ.Whenqi=0, there is a special case and there is no wrong classification. Whenqi=1, the opposite is true.Sinceyi is a misclassified observation, the probability of yi being symmetrically correctly classified is as follows:
(8)Pyi=0∣ui=0=Pyi=1∣ui=1=1−qi.The likelihood function of logistic regression is as follows:(9)Lβ=Πj=1nPryj∣xj=Πj=1npxj,β,qjyj1−pxj,β,qj1−yj=Πj=1n1−qjexpxjTβ+qj1+expxjTβyj1−qj+qjexpxjTβ1+expxjTβ1−yj..Take the logarithm and the formula is as follows:(10)lβ=∑j=1nyjIn1−qjexpxjTβ+qj1+expxjTβ+qj+1−yjIn1−qjexpxjTβ1+expxjTβ+qj.Formula (10) derives β and makes the derivative equal to zero, resulting in the scoring equation as follows:
(11)∑j=1nyj1−qjexpxjTβ1−qjexpxjTβ+qj+1−yjqjexpxjTβqjexpxjTβ+1−qj−expxjTβ1+expxjTβxj=0.The value of the differential ofβ to qj at qj=0 is as follows:
(12)∂β^i∂qiqi=1−2yiexp1−2yixiTβ^H−1xi.In the above formula, the expression forH is
(13)H=∑j=1nexpxjTβ1+expxjTβ2xjxjT.


#### 3.1.3. Prediction Probability Sensitivity

Logistic regression model mainly predicts whether it is misclassified by the size ofpiβ. Therefore, after estimating the coefficient, the prediction ability of the model can be verified according to the prediction probability piβ, and the formula is as follows:
(14)p^=expxjTβ1+expxjTβ.The sensitivity of wrong classification is analyzed, and the derivative of prediction probabilityp with respect to classification probability p^ at qi=0 is taken as prediction probability, and the formula is as follows:
(15)∂p^i∂qiqi=0=∂p∧i∂β∧qi=0′∂β^i∂qiqi=0=1−2yiexp21−yjxjTβ1+expxjTβ2.


### 3.2. Subsampling Algorithm

Based on the gradient expression of loss function estimated by least squares, a self-adaptive gradient subsampling algorithm is proposed. The main steps of the algorithm are as follows.Find the loss function of the logistic regression model of the setxi,yii=1n, and the formula is as follows:
(16)ϑβ,xi=1n∑i=1nyilogpβxi+log1−pβxi.The loss function is derived fromβ:
(17)∂ϑβ,xi∂β=1n∑i=1nyi−pβxixi.The gradient formula of loss function of thei test sample is as follows:
(18)δi=yi−pβxixi.The definition of subsampling probability of thei test sample for loss function gradient is as follows:
(19)πiδ=δi∑i=1nδi.


#### 3.2.1. Optimized Subsampling Algorithm

The asymptotic properties of weighted gradient subsampling maximum likelihood estimators for logistic regression models are studied. Given the total samplexi,yii=1n, when the response variable satisfies certain conditions, r is the subsample size, and when n → ∞, r → ∞, the formula is as follows:
(20)V−1/2β~−β^MLB⟶N0,1.In the above formula, the expressions forV and Vc are as follows:
(21)V=MX−1VcMX−1,Vc=1rn2∑i=1nyi−β∧MLB2xixiTπi.The asymptotic mean square error ofβ~ is equivalent to the trace of matrix V, that is,
(22)AMSEβ~=trV.Optimize according to the thinking mode of “A-optimality”:(23)minAMSEβ~=mintrVtrVtakes the minimum value, and the sampling probability is as follows:
(24)πimMSE=yi−β^MLBMX−1xi∑i=1nyi−β^MLBMX−1xi.Optimize the problem and get(25)πiMVE=yi−β^MLBMX−1xi∑i=1nyi−β^MLBMX−1xi.


## 3.1. Logistic Regression Model

### 3.1.1. General Linear Regression Model

In statistical analysis with less fluctuation, the lever value of general linear model is defined as shown in the formula:(1)hii=XXTX−1Xi,where hii is the i-th diagonal element of the matrix H=XXTX−1Xi.The target value is estimated by the least squares of the general linear model, and the predicted valuey^ican be written as follows:
(2)y^i=∑j=1nhijyj=hiiyi+∑j≠inhijyj.y^takes the partial derivative of yi, as follows:
(3)∂y^i∂yi=hii,i=1,2,⋯,n.The subsampling algorithm of general linear model is an important subsampling strategy based on normalized empirical statistical lever score of input matrixX. The calculation of sampling probability distribution is as follows:
(4)πIleverage=hii∑i=1nhii,i=1,2,..n.


### 3.1.2. Sensitivity to Misclassification

The formula for the logistic regression model is defined as follows:(5)Pyi=1∣xi=piβ=expxiTβ1+expxiTβ.When a certaini is selected, it is assumed that the tested event yiis misclassified symmetrically with the probability qi and that all other observations are correctly classified, that is, the probability of misclassification of these observations is qi =0, j≠i. If the sensitivity to error classification is calculated in the presence of error classification, the probability distribution of response variables is recorded as the following formula:
(6)Pyi=1ui=0=Pyi=0∣ui=1=qi.The marginal probability of observed eventyi=1 is obtained as follows:
(7)Pyi=1=Pyi=1∣ui=0+Pyi=1∣ui=1=1−qjexpxjTβ+qj1+expxjTβ.Whenqi=0, there is a special case and there is no wrong classification. Whenqi=1, the opposite is true.Sinceyi is a misclassified observation, the probability of yi being symmetrically correctly classified is as follows:
(8)Pyi=0∣ui=0=Pyi=1∣ui=1=1−qi.The likelihood function of logistic regression is as follows:(9)Lβ=Πj=1nPryj∣xj=Πj=1npxj,β,qjyj1−pxj,β,qj1−yj=Πj=1n1−qjexpxjTβ+qj1+expxjTβyj1−qj+qjexpxjTβ1+expxjTβ1−yj..Take the logarithm and the formula is as follows:(10)lβ=∑j=1nyjIn1−qjexpxjTβ+qj1+expxjTβ+qj+1−yjIn1−qjexpxjTβ1+expxjTβ+qj.Formula (10) derives β and makes the derivative equal to zero, resulting in the scoring equation as follows:
(11)∑j=1nyj1−qjexpxjTβ1−qjexpxjTβ+qj+1−yjqjexpxjTβqjexpxjTβ+1−qj−expxjTβ1+expxjTβxj=0.The value of the differential ofβ to qj at qj=0 is as follows:
(12)∂β^i∂qiqi=1−2yiexp1−2yixiTβ^H−1xi.In the above formula, the expression forH is
(13)H=∑j=1nexpxjTβ1+expxjTβ2xjxjT.


### 3.1.3. Prediction Probability Sensitivity

Logistic regression model mainly predicts whether it is misclassified by the size ofpiβ. Therefore, after estimating the coefficient, the prediction ability of the model can be verified according to the prediction probability piβ, and the formula is as follows:
(14)p^=expxjTβ1+expxjTβ.The sensitivity of wrong classification is analyzed, and the derivative of prediction probabilityp with respect to classification probability p^ at qi=0 is taken as prediction probability, and the formula is as follows:
(15)∂p^i∂qiqi=0=∂p∧i∂β∧qi=0′∂β^i∂qiqi=0=1−2yiexp21−yjxjTβ1+expxjTβ2.


## 3.1.1. General Linear Regression Model

In statistical analysis with less fluctuation, the lever value of general linear model is defined as shown in the formula:(1)hii=XXTX−1Xi,where hii is the i-th diagonal element of the matrix H=XXTX−1Xi.The target value is estimated by the least squares of the general linear model, and the predicted valuey^ican be written as follows:
(2)y^i=∑j=1nhijyj=hiiyi+∑j≠inhijyj.y^takes the partial derivative of yi, as follows:
(3)∂y^i∂yi=hii,i=1,2,⋯,n.The subsampling algorithm of general linear model is an important subsampling strategy based on normalized empirical statistical lever score of input matrixX. The calculation of sampling probability distribution is as follows:
(4)πIleverage=hii∑i=1nhii,i=1,2,..n.


## 3.1.2. Sensitivity to Misclassification

The formula for the logistic regression model is defined as follows:(5)Pyi=1∣xi=piβ=expxiTβ1+expxiTβ.When a certaini is selected, it is assumed that the tested event yiis misclassified symmetrically with the probability qi and that all other observations are correctly classified, that is, the probability of misclassification of these observations is qi =0, j≠i. If the sensitivity to error classification is calculated in the presence of error classification, the probability distribution of response variables is recorded as the following formula:
(6)Pyi=1ui=0=Pyi=0∣ui=1=qi.The marginal probability of observed eventyi=1 is obtained as follows:
(7)Pyi=1=Pyi=1∣ui=0+Pyi=1∣ui=1=1−qjexpxjTβ+qj1+expxjTβ.Whenqi=0, there is a special case and there is no wrong classification. Whenqi=1, the opposite is true.Sinceyi is a misclassified observation, the probability of yi being symmetrically correctly classified is as follows:
(8)Pyi=0∣ui=0=Pyi=1∣ui=1=1−qi.The likelihood function of logistic regression is as follows:(9)Lβ=Πj=1nPryj∣xj=Πj=1npxj,β,qjyj1−pxj,β,qj1−yj=Πj=1n1−qjexpxjTβ+qj1+expxjTβyj1−qj+qjexpxjTβ1+expxjTβ1−yj..Take the logarithm and the formula is as follows:(10)lβ=∑j=1nyjIn1−qjexpxjTβ+qj1+expxjTβ+qj+1−yjIn1−qjexpxjTβ1+expxjTβ+qj.Formula (10) derives β and makes the derivative equal to zero, resulting in the scoring equation as follows:
(11)∑j=1nyj1−qjexpxjTβ1−qjexpxjTβ+qj+1−yjqjexpxjTβqjexpxjTβ+1−qj−expxjTβ1+expxjTβxj=0.The value of the differential ofβ to qj at qj=0 is as follows:
(12)∂β^i∂qiqi=1−2yiexp1−2yixiTβ^H−1xi.In the above formula, the expression forH is
(13)H=∑j=1nexpxjTβ1+expxjTβ2xjxjT.


## 3.1.3. Prediction Probability Sensitivity

Logistic regression model mainly predicts whether it is misclassified by the size ofpiβ. Therefore, after estimating the coefficient, the prediction ability of the model can be verified according to the prediction probability piβ, and the formula is as follows:
(14)p^=expxjTβ1+expxjTβ.The sensitivity of wrong classification is analyzed, and the derivative of prediction probabilityp with respect to classification probability p^ at qi=0 is taken as prediction probability, and the formula is as follows:
(15)∂p^i∂qiqi=0=∂p∧i∂β∧qi=0′∂β^i∂qiqi=0=1−2yiexp21−yjxjTβ1+expxjTβ2.


## 3.2. Subsampling Algorithm

Based on the gradient expression of loss function estimated by least squares, a self-adaptive gradient subsampling algorithm is proposed. The main steps of the algorithm are as follows.Find the loss function of the logistic regression model of the setxi,yii=1n, and the formula is as follows:
(16)ϑβ,xi=1n∑i=1nyilogpβxi+log1−pβxi.The loss function is derived fromβ:
(17)∂ϑβ,xi∂β=1n∑i=1nyi−pβxixi.The gradient formula of loss function of thei test sample is as follows:
(18)δi=yi−pβxixi.The definition of subsampling probability of thei test sample for loss function gradient is as follows:
(19)πiδ=δi∑i=1nδi.


### 3.2.1. Optimized Subsampling Algorithm

The asymptotic properties of weighted gradient subsampling maximum likelihood estimators for logistic regression models are studied. Given the total samplexi,yii=1n, when the response variable satisfies certain conditions, r is the subsample size, and when n → ∞, r → ∞, the formula is as follows:
(20)V−1/2β~−β^MLB⟶N0,1.In the above formula, the expressions forV and Vc are as follows:
(21)V=MX−1VcMX−1,Vc=1rn2∑i=1nyi−β∧MLB2xixiTπi.The asymptotic mean square error ofβ~ is equivalent to the trace of matrix V, that is,
(22)AMSEβ~=trV.Optimize according to the thinking mode of “A-optimality”:(23)minAMSEβ~=mintrVtrVtakes the minimum value, and the sampling probability is as follows:
(24)πimMSE=yi−β^MLBMX−1xi∑i=1nyi−β^MLBMX−1xi.Optimize the problem and get(25)πiMVE=yi−β^MLBMX−1xi∑i=1nyi−β^MLBMX−1xi.


## 3.2.1. Optimized Subsampling Algorithm

The asymptotic properties of weighted gradient subsampling maximum likelihood estimators for logistic regression models are studied. Given the total samplexi,yii=1n, when the response variable satisfies certain conditions, r is the subsample size, and when n → ∞, r → ∞, the formula is as follows:
(20)V−1/2β~−β^MLB⟶N0,1.In the above formula, the expressions forV and Vc are as follows:
(21)V=MX−1VcMX−1,Vc=1rn2∑i=1nyi−β∧MLB2xixiTπi.The asymptotic mean square error ofβ~ is equivalent to the trace of matrix V, that is,
(22)AMSEβ~=trV.Optimize according to the thinking mode of “A-optimality”:(23)minAMSEβ~=mintrVtrVtakes the minimum value, and the sampling probability is as follows:
(24)πimMSE=yi−β^MLBMX−1xi∑i=1nyi−β^MLBMX−1xi.Optimize the problem and get(25)πiMVE=yi−β^MLBMX−1xi∑i=1nyi−β^MLBMX−1xi.


## 4. Experiment

### 4.1. Sample Population Selection

In the logistic regression model, samples are the most important step in the experimental study. Samples are selected from three groups: excessive sports, moderate sports, occasional sports, and never sports. The differences in sample selection also affect the final analysis results. The sample used in this study is the middle-aged and elderly people of similar age in the community. According to their sports situation, the correlation between sports and aging is analyzed.


### 4.2. Experimental Testing

We know that exercise can improve the activity of protein mitochondria and exercise can improve the activity expression of Sirt1 and the ability of antioxidant system. Below, we use logistic regression model to test and count the aging of the sample population by sports, as shown in Figure1.Figure 1
Protein activity bar chart.From Figure1, we can know that the longevity factor Sirt1 has the highest activity in moderate exercise and can achieve antiaging effect more than other degrees of exercise. Based on logistic regression model, we count the central nervous system, cardiopulmonary system, and digestive system of the sample population based on medical big data as follows:The performance of each part of the central nervous system of the sample population is shown in Table2.Table 2
Manifestations of various parts of central nervous system.
Sample populationCerebellumCerebellumBrainstemSpinal cordLive sportsExcellentGoodGeneralGeneralModerate exerciseExcellentExcellentExcellentExcellentOccasional exerciseExcellentGoodGoodGoodNever exerciseExcellentGeneralGeneralGeneralFor the elderly with Alzheimer’s disease, the statistical central nervous system situation after one month of appropriate exercise is shown in Figure2.Figure 2
Central nervous system.After a month of proper exercise, the central nervous system has obviously improved, and the physical function has gradually improved.Based on the cardiopulmonary function of the sample population with medical big data, the statistical data are shown in Table3.Table 3
Cardiopulmonary function.
Sample populationCardiopulmonary indexLoad peak/wVO2/ml/minHRR/times/pointsPulse accounts for the predicted valueLive sports130.2±38.11360.21±340.0237.1±17.2392.1±20.01Moderate exercise117.3±36.11190.21±335.0227.3±15.2182.1±11.01Occasional exercise122.2±37.21290.41±338.0932.1±17.1187.1±20.61Never exercise131.2±41.11490.21±345.0138.1±19.2192.3±23.01For the elderly with poor cardiopulmonary function, the statistical changes of cardiopulmonary indexes after one month’s appropriate exercise are shown in Figure3.Figure 3
Cardiopulmonary function diagram.Figure3 shows that proper exercise is also of great help to the elderly with poor cardiopulmonary function, and various indicators are also developing in this good direction.Statistics of digestive system indicators of sample population based on medical big data is shown in Table4.Table 4
Digestive system.
Sample populationDigestive system indexCarcinoembryonic antigenAlpha-fetoproteinCarbohydrate antigenCarbohydrate antigen242Live sports5.110.127.117Moderate exercise4.48.225.215.1Occasional exercise5.09.827.317.1Never exercise5.51228.217.5Based on medical big data, the sports situation of the elderly suffering from digestive diseases in the community is counted, as shown in Figure4.Figure 4
Pie chart of digestive tract diseases.


### 4.3. Model Comparison

We compare and analyze the logistic regression model with the subsampling model algorithm and the large sample model algorithm and analyze the correlation analysis of sports against aging through the algorithm as shown in Figure5.Figure 5
Model comparison diagram.


### 4.4. Experimental Analysis

Based on the specific research and analysis of sports and antiaging, in order to highlight the great role of sports in human aging, we selected some aging monkeys as samples, and we compared four schemes:Scheme 1: The aging monkeys were overtrained every day, and the experimental training lasted for 3 monthsScheme 2: Training aging monkeys occasionally for 3 monthsScheme 3: The aging monkeys should exercise properly every day for 3 monthsScheme 4: The aging monkeys only watch TV every day without any training. After 3 months, we will count the heart and lung conditions of aging monkeys as shown in Figure6Figure 6
Schematic diagram of cardiopulmonary indexes.After the experimental comparison of the four schemes, only the third scheme has the smoothest and most stable cardiopulmonary indexes.The experimental data obtained after several months of training are shown in Figure7.Figure 7
Comparison diagram of experimental rats.According to the experimental comparison of five indexes of aging monkeys, the five indexes of monkeys with suitable training are indeed superior to those of monkeys with other exercise degrees, which fully confirms our experimental goals.


### 4.5. Contrast Test

According to the logistic regression model in the paper, we classify and compare the intensity of sports. The sports with the greatest intensity are set as rock climbing, followed by basketball, tai chi, and TV. The relationship between aging and aging is analyzed, as shown in Table5.Table 5
Comparative experiment.
Sample populationLogistic regression modelNumber of illnessesHealth coefficientCell senescenceSirt1 activityRock climbing31.00.9StrongerPlay basketball10.80.7StrongerPlay tai chi00.60.8StrongWatch TV31.21.5WeakTable5 lists the analysis data of aging caused by liking four different intensity sports based on logistic regression model. It can be clearly seen that not loving strenuous exercise is the most effective in antiaging, but doing some suitable physical exercise can delay the aging of the body. Of course, watching TV every day without exercise will not delay the aging of the body.For the influence on cognitive function of the elderly with different exercise intensity, the experiment was carried out for 6 months (schematic Figure8).Figure 8
Influence of exercise on cognitive function of the elderly.


## 4.1. Sample Population Selection

In the logistic regression model, samples are the most important step in the experimental study. Samples are selected from three groups: excessive sports, moderate sports, occasional sports, and never sports. The differences in sample selection also affect the final analysis results. The sample used in this study is the middle-aged and elderly people of similar age in the community. According to their sports situation, the correlation between sports and aging is analyzed.


## 4.2. Experimental Testing

We know that exercise can improve the activity of protein mitochondria and exercise can improve the activity expression of Sirt1 and the ability of antioxidant system. Below, we use logistic regression model to test and count the aging of the sample population by sports, as shown in Figure1.Figure 1
Protein activity bar chart.From Figure1, we can know that the longevity factor Sirt1 has the highest activity in moderate exercise and can achieve antiaging effect more than other degrees of exercise. Based on logistic regression model, we count the central nervous system, cardiopulmonary system, and digestive system of the sample population based on medical big data as follows:The performance of each part of the central nervous system of the sample population is shown in Table2.Table 2
Manifestations of various parts of central nervous system.
Sample populationCerebellumCerebellumBrainstemSpinal cordLive sportsExcellentGoodGeneralGeneralModerate exerciseExcellentExcellentExcellentExcellentOccasional exerciseExcellentGoodGoodGoodNever exerciseExcellentGeneralGeneralGeneralFor the elderly with Alzheimer’s disease, the statistical central nervous system situation after one month of appropriate exercise is shown in Figure2.Figure 2
Central nervous system.After a month of proper exercise, the central nervous system has obviously improved, and the physical function has gradually improved.Based on the cardiopulmonary function of the sample population with medical big data, the statistical data are shown in Table3.Table 3
Cardiopulmonary function.
Sample populationCardiopulmonary indexLoad peak/wVO2/ml/minHRR/times/pointsPulse accounts for the predicted valueLive sports130.2±38.11360.21±340.0237.1±17.2392.1±20.01Moderate exercise117.3±36.11190.21±335.0227.3±15.2182.1±11.01Occasional exercise122.2±37.21290.41±338.0932.1±17.1187.1±20.61Never exercise131.2±41.11490.21±345.0138.1±19.2192.3±23.01For the elderly with poor cardiopulmonary function, the statistical changes of cardiopulmonary indexes after one month’s appropriate exercise are shown in Figure3.Figure 3
Cardiopulmonary function diagram.Figure3 shows that proper exercise is also of great help to the elderly with poor cardiopulmonary function, and various indicators are also developing in this good direction.Statistics of digestive system indicators of sample population based on medical big data is shown in Table4.Table 4
Digestive system.
Sample populationDigestive system indexCarcinoembryonic antigenAlpha-fetoproteinCarbohydrate antigenCarbohydrate antigen242Live sports5.110.127.117Moderate exercise4.48.225.215.1Occasional exercise5.09.827.317.1Never exercise5.51228.217.5Based on medical big data, the sports situation of the elderly suffering from digestive diseases in the community is counted, as shown in Figure4.Figure 4
Pie chart of digestive tract diseases.


## 4.3. Model Comparison

We compare and analyze the logistic regression model with the subsampling model algorithm and the large sample model algorithm and analyze the correlation analysis of sports against aging through the algorithm as shown in Figure5.Figure 5
Model comparison diagram.


## 4.4. Experimental Analysis

Based on the specific research and analysis of sports and antiaging, in order to highlight the great role of sports in human aging, we selected some aging monkeys as samples, and we compared four schemes:Scheme 1: The aging monkeys were overtrained every day, and the experimental training lasted for 3 monthsScheme 2: Training aging monkeys occasionally for 3 monthsScheme 3: The aging monkeys should exercise properly every day for 3 monthsScheme 4: The aging monkeys only watch TV every day without any training. After 3 months, we will count the heart and lung conditions of aging monkeys as shown in Figure6Figure 6
Schematic diagram of cardiopulmonary indexes.After the experimental comparison of the four schemes, only the third scheme has the smoothest and most stable cardiopulmonary indexes.The experimental data obtained after several months of training are shown in Figure7.Figure 7
Comparison diagram of experimental rats.According to the experimental comparison of five indexes of aging monkeys, the five indexes of monkeys with suitable training are indeed superior to those of monkeys with other exercise degrees, which fully confirms our experimental goals.


## 4.5. Contrast Test

According to the logistic regression model in the paper, we classify and compare the intensity of sports. The sports with the greatest intensity are set as rock climbing, followed by basketball, tai chi, and TV. The relationship between aging and aging is analyzed, as shown in Table5.Table 5
Comparative experiment.
Sample populationLogistic regression modelNumber of illnessesHealth coefficientCell senescenceSirt1 activityRock climbing31.00.9StrongerPlay basketball10.80.7StrongerPlay tai chi00.60.8StrongWatch TV31.21.5WeakTable5 lists the analysis data of aging caused by liking four different intensity sports based on logistic regression model. It can be clearly seen that not loving strenuous exercise is the most effective in antiaging, but doing some suitable physical exercise can delay the aging of the body. Of course, watching TV every day without exercise will not delay the aging of the body.For the influence on cognitive function of the elderly with different exercise intensity, the experiment was carried out for 6 months (schematic Figure8).Figure 8
Influence of exercise on cognitive function of the elderly.


## 5. Conclusion

The increasing aging population leads to the gradual aggravation of the aging problem. Nowadays, our country has become a country of “getting old before getting rich,” and aging has brought a series of social problems that need us to solve. For personal health, how to keep a healthy body and delay one’s own aging is the most important thing. Through logistic regression model, this paper studies and analyzes sports and aging:(1)
We compare logistic regression model with subsample algorithm and large sample algorithm and obviously draw the conclusion that logistic regression model has more statistical significance(2)
Based on the use of medical big data, we can know that patients with poor cardiopulmonary function, digestive function, and nerve center function have improved through proper exercise(3)
For people who do not exercise, they suffer from digestive tract diseases as high as 70%, and proper exercise can improve the cognitive function of the elderly



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*Source: 3810676-2022-07-07.xml*