Prediction of atherosclerosis using machine learning based on operations research
Background: Atherosclerosis is one of the major reasons for cardiovascular disease including coronary heart disease, cerebral infarction and peripheral vascular disease. Atherosclerosis has no obvious symptoms in its early stages, so the key to the treatment of atherosclerosis is early intervention...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
AIMS Press
2022-03-01
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| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2022229?viewType=HTML |
| _version_ | 1828873838262222848 |
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| author | Zihan Chen Minhui Yang Yuhang Wen Songyan Jiang Wenjun Liu Hui Huang |
| author_facet | Zihan Chen Minhui Yang Yuhang Wen Songyan Jiang Wenjun Liu Hui Huang |
| author_sort | Zihan Chen |
| collection | DOAJ |
| description | Background:
Atherosclerosis is one of the major reasons for cardiovascular disease including coronary heart disease, cerebral infarction and peripheral vascular disease. Atherosclerosis has no obvious symptoms in its early stages, so the key to the treatment of atherosclerosis is early intervention of risk factors. Machine learning methods have been used to predict atherosclerosis, but the presence of strong causal relationships between features can lead to extremely high levels of information redundancy, which can affect the effectiveness of prediction systems.
Objective:
We aim to combine statistical analysis and machine learning methods to reduce information redundancy and further improve the accuracy of disease diagnosis.
Methods:
We cleaned and collated the relevant data obtained from the retrospective study at Affiliated Hospital of Nanjing University of Chinese Medicine through data analysis. First, some features that with too many missing values are filtered out of the 34 features, leaving 25 features. 49% of the samples were categorized as the atherosclerosis risk group while the rest 51% as the control group without atherosclerosis risk under the guidance of relevant experts. We compared the prediction results of a single indicator that had been medically proven to be highly correlated with atherosclerosis with the prediction results of multiple features to fully demonstrate the effect of feature information redundancy on the prediction results. Then the features that could distinguish whether have atherosclerosis risk or not were retained by statistical tests, leaving 20 features. To reduce the information redundancy between features, after drawing inspiration from graph theory, machine learning combined with optimal correlation distances was then used to screen out 15 significant features, and the prediction models were evaluated under the 15 features. Finally, the information of the 5 screened-out non-significant features was fully utilized by ensemble learning to improve the prediction superiority for atherosclerosis.
Results:
Area Under the Receiver Operating Characteristic (ROC) Curve (AUC), which is used to measure the predictive performance of the model, was 0.84035 and Kolmogorov-Smirnov (KS) value was 0.646. After feature selection model based on optimal correlation distance, the AUC value was 0.88268 and the KS value was 0.688, both of which were improved by about 0.04. Finally, after ensemble learning, the AUC value of the model was further improved by 0.01369 to 0.89637.
Conclusions:
The optimal distance feature screening model proposed in this paper improves the performance of atherosclerosis prediction models in terms of both prediction accuracy and AUC metrics. Code and models are available at https://github.com/Cesartwothousands/Prediction-of-Atherosclerosis. |
| first_indexed | 2024-12-13T07:24:20Z |
| format | Article |
| id | doaj.art-15dc0b27cd074781bee3447867ef86cf |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-12-13T07:24:20Z |
| publishDate | 2022-03-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-15dc0b27cd074781bee3447867ef86cf2022-12-21T23:55:21ZengAIMS PressMathematical Biosciences and Engineering1551-00182022-03-011954892491010.3934/mbe.2022229Prediction of atherosclerosis using machine learning based on operations researchZihan Chen0Minhui Yang1Yuhang Wen 2Songyan Jiang 3Wenjun Liu4Hui Huang 51. Changwang School of Honors, Nanjing University of Information Science and Technology, Nanjing 210044, China2. School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China3. School of Teacher Education, Nanjing University of Information Science and Technology, Nanjing 210044, China3. School of Teacher Education, Nanjing University of Information Science and Technology, Nanjing 210044, China4. School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China5. Department of Ultrasound, Affiliated Hospital of Nanjing University of CM, Nanjing 210029, ChinaBackground: Atherosclerosis is one of the major reasons for cardiovascular disease including coronary heart disease, cerebral infarction and peripheral vascular disease. Atherosclerosis has no obvious symptoms in its early stages, so the key to the treatment of atherosclerosis is early intervention of risk factors. Machine learning methods have been used to predict atherosclerosis, but the presence of strong causal relationships between features can lead to extremely high levels of information redundancy, which can affect the effectiveness of prediction systems. Objective: We aim to combine statistical analysis and machine learning methods to reduce information redundancy and further improve the accuracy of disease diagnosis. Methods: We cleaned and collated the relevant data obtained from the retrospective study at Affiliated Hospital of Nanjing University of Chinese Medicine through data analysis. First, some features that with too many missing values are filtered out of the 34 features, leaving 25 features. 49% of the samples were categorized as the atherosclerosis risk group while the rest 51% as the control group without atherosclerosis risk under the guidance of relevant experts. We compared the prediction results of a single indicator that had been medically proven to be highly correlated with atherosclerosis with the prediction results of multiple features to fully demonstrate the effect of feature information redundancy on the prediction results. Then the features that could distinguish whether have atherosclerosis risk or not were retained by statistical tests, leaving 20 features. To reduce the information redundancy between features, after drawing inspiration from graph theory, machine learning combined with optimal correlation distances was then used to screen out 15 significant features, and the prediction models were evaluated under the 15 features. Finally, the information of the 5 screened-out non-significant features was fully utilized by ensemble learning to improve the prediction superiority for atherosclerosis. Results: Area Under the Receiver Operating Characteristic (ROC) Curve (AUC), which is used to measure the predictive performance of the model, was 0.84035 and Kolmogorov-Smirnov (KS) value was 0.646. After feature selection model based on optimal correlation distance, the AUC value was 0.88268 and the KS value was 0.688, both of which were improved by about 0.04. Finally, after ensemble learning, the AUC value of the model was further improved by 0.01369 to 0.89637. Conclusions: The optimal distance feature screening model proposed in this paper improves the performance of atherosclerosis prediction models in terms of both prediction accuracy and AUC metrics. Code and models are available at https://github.com/Cesartwothousands/Prediction-of-Atherosclerosis.https://www.aimspress.com/article/doi/10.3934/mbe.2022229?viewType=HTMLatherosclerosismachine learningrandom forest classifierensemble learningoperation researchinformation redundancy |
| spellingShingle | Zihan Chen Minhui Yang Yuhang Wen Songyan Jiang Wenjun Liu Hui Huang Prediction of atherosclerosis using machine learning based on operations research Mathematical Biosciences and Engineering atherosclerosis machine learning random forest classifier ensemble learning operation research information redundancy |
| title | Prediction of atherosclerosis using machine learning based on operations research |
| title_full | Prediction of atherosclerosis using machine learning based on operations research |
| title_fullStr | Prediction of atherosclerosis using machine learning based on operations research |
| title_full_unstemmed | Prediction of atherosclerosis using machine learning based on operations research |
| title_short | Prediction of atherosclerosis using machine learning based on operations research |
| title_sort | prediction of atherosclerosis using machine learning based on operations research |
| topic | atherosclerosis machine learning random forest classifier ensemble learning operation research information redundancy |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2022229?viewType=HTML |
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