Early Prediction in Classification of Cardiovascular Diseases with Machine Learning, Neuro-Fuzzy and Statistical Methods
Timely and accurate detection of cardiovascular diseases (CVDs) is critically important to minimize the risk of a myocardial infarction. Relations between factors of CVDs are complex, ill-defined and nonlinear, justifying the use of artificial intelligence tools. These tools aid in predicting and cl...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Biology |
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| Online Access: | https://www.mdpi.com/2079-7737/12/1/117 |
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| author | Osman Taylan Abdulaziz S. Alkabaa Hanan S. Alqabbaa Esra Pamukçu Víctor Leiva |
| author_facet | Osman Taylan Abdulaziz S. Alkabaa Hanan S. Alqabbaa Esra Pamukçu Víctor Leiva |
| author_sort | Osman Taylan |
| collection | DOAJ |
| description | Timely and accurate detection of cardiovascular diseases (CVDs) is critically important to minimize the risk of a myocardial infarction. Relations between factors of CVDs are complex, ill-defined and nonlinear, justifying the use of artificial intelligence tools. These tools aid in predicting and classifying CVDs. In this article, we propose a methodology using machine learning (ML) approaches to predict, classify and improve the diagnostic accuracy of CVDs, including support vector regression (SVR), multivariate adaptive regression splines, the M5Tree model and neural networks for the training process. Moreover, adaptive neuro-fuzzy and statistical approaches, nearest neighbor/naive Bayes classifiers and adaptive neuro-fuzzy inference system (ANFIS) are used to predict seventeen CVD risk factors. Mixed-data transformation and classification methods are employed for categorical and continuous variables predicting CVD risk. We compare our hybrid models and existing ML techniques on a CVD real dataset collected from a hospital. A sensitivity analysis is performed to determine the influence and exhibit the essential variables with regard to CVDs, such as the patient’s age, cholesterol level and glucose level. Our results report that the proposed methodology outperformed well known statistical and ML approaches, showing their versatility and utility in CVD classification. Our investigation indicates that the prediction accuracy of ANFIS for the training process is 96.56%, followed by SVR with 91.95% prediction accuracy. Our study includes a comprehensive comparison of results obtained for the mentioned methods. |
| first_indexed | 2024-03-09T13:31:37Z |
| format | Article |
| id | doaj.art-dafd4c0db6b2452fbf38de16f8586217 |
| institution | Directory Open Access Journal |
| issn | 2079-7737 |
| language | English |
| last_indexed | 2024-03-09T13:31:37Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biology |
| spelling | doaj.art-dafd4c0db6b2452fbf38de16f85862172023-11-30T21:17:38ZengMDPI AGBiology2079-77372023-01-0112111710.3390/biology12010117Early Prediction in Classification of Cardiovascular Diseases with Machine Learning, Neuro-Fuzzy and Statistical MethodsOsman Taylan0Abdulaziz S. Alkabaa1Hanan S. Alqabbaa2Esra Pamukçu3Víctor Leiva4Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi ArabiaDepartment of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi ArabiaUniversity Medical Services Center, King Abdulaziz University, Jeddah 21589, Saudi ArabiaDepartment of Statistics, Firat University, 23119 Elazığ, TurkeySchool of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, ChileTimely and accurate detection of cardiovascular diseases (CVDs) is critically important to minimize the risk of a myocardial infarction. Relations between factors of CVDs are complex, ill-defined and nonlinear, justifying the use of artificial intelligence tools. These tools aid in predicting and classifying CVDs. In this article, we propose a methodology using machine learning (ML) approaches to predict, classify and improve the diagnostic accuracy of CVDs, including support vector regression (SVR), multivariate adaptive regression splines, the M5Tree model and neural networks for the training process. Moreover, adaptive neuro-fuzzy and statistical approaches, nearest neighbor/naive Bayes classifiers and adaptive neuro-fuzzy inference system (ANFIS) are used to predict seventeen CVD risk factors. Mixed-data transformation and classification methods are employed for categorical and continuous variables predicting CVD risk. We compare our hybrid models and existing ML techniques on a CVD real dataset collected from a hospital. A sensitivity analysis is performed to determine the influence and exhibit the essential variables with regard to CVDs, such as the patient’s age, cholesterol level and glucose level. Our results report that the proposed methodology outperformed well known statistical and ML approaches, showing their versatility and utility in CVD classification. Our investigation indicates that the prediction accuracy of ANFIS for the training process is 96.56%, followed by SVR with 91.95% prediction accuracy. Our study includes a comprehensive comparison of results obtained for the mentioned methods.https://www.mdpi.com/2079-7737/12/1/117adaptive neuro-fuzzy inference systemartificial intelligencebioinformaticscardiovascular diseasesclassificationelastic net |
| spellingShingle | Osman Taylan Abdulaziz S. Alkabaa Hanan S. Alqabbaa Esra Pamukçu Víctor Leiva Early Prediction in Classification of Cardiovascular Diseases with Machine Learning, Neuro-Fuzzy and Statistical Methods Biology adaptive neuro-fuzzy inference system artificial intelligence bioinformatics cardiovascular diseases classification elastic net |
| title | Early Prediction in Classification of Cardiovascular Diseases with Machine Learning, Neuro-Fuzzy and Statistical Methods |
| title_full | Early Prediction in Classification of Cardiovascular Diseases with Machine Learning, Neuro-Fuzzy and Statistical Methods |
| title_fullStr | Early Prediction in Classification of Cardiovascular Diseases with Machine Learning, Neuro-Fuzzy and Statistical Methods |
| title_full_unstemmed | Early Prediction in Classification of Cardiovascular Diseases with Machine Learning, Neuro-Fuzzy and Statistical Methods |
| title_short | Early Prediction in Classification of Cardiovascular Diseases with Machine Learning, Neuro-Fuzzy and Statistical Methods |
| title_sort | early prediction in classification of cardiovascular diseases with machine learning neuro fuzzy and statistical methods |
| topic | adaptive neuro-fuzzy inference system artificial intelligence bioinformatics cardiovascular diseases classification elastic net |
| url | https://www.mdpi.com/2079-7737/12/1/117 |
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