A classification and regression tree algorithm for heart disease modeling and prediction
Heart disease remains the leading cause of death, such that nearly one-third of all deaths worldwide are estimated to be caused by heart-related conditions. Advancing applications of classification-based machine learning to medicine facilitates earlier detection. In this study, the Classification an...
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| Veröffentlicht in: | Healthcare analytics (New York, N.Y.) Jg. 3; S. 100130 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.11.2023
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| ISSN: | 2772-4425, 2772-4425 |
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| Abstract | Heart disease remains the leading cause of death, such that nearly one-third of all deaths worldwide are estimated to be caused by heart-related conditions. Advancing applications of classification-based machine learning to medicine facilitates earlier detection. In this study, the Classification and Regression Tree (CART) algorithm, a supervised machine learning method, has been employed to predict heart disease and extract decision rules in clarifying relationships between input and output variables. In addition, the study’s findings rank the features influencing heart disease based on importance. When considering all performance parameters, the 87% accuracy of the prediction validates the model’s reliability. On the other hand, extracted decision rules reported in the study can simplify the use of clinical purposes without needing additional knowledge. Overall, the proposed algorithm can support not only healthcare professionals but patients who are subjected to cost and time constraints in the diagnosis and treatment processes of heart disease.
•We employ a decision tree algorithm to model and predict heart disease.•We build and train the decision model using electronic health records data of 1190 patients.•On the basis of patients’ characteristics and using decision tree analysis, IF–THEN rules are extracted.•The importance of features in the decision tree analysis has been investigated.•The Classification and Regression Tree (CART) algorithm performs reasonably well in predicting heart disease. |
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| AbstractList | Heart disease remains the leading cause of death, such that nearly one-third of all deaths worldwide are estimated to be caused by heart-related conditions. Advancing applications of classification-based machine learning to medicine facilitates earlier detection. In this study, the Classification and Regression Tree (CART) algorithm, a supervised machine learning method, has been employed to predict heart disease and extract decision rules in clarifying relationships between input and output variables. In addition, the study’s findings rank the features influencing heart disease based on importance. When considering all performance parameters, the 87% accuracy of the prediction validates the model’s reliability. On the other hand, extracted decision rules reported in the study can simplify the use of clinical purposes without needing additional knowledge. Overall, the proposed algorithm can support not only healthcare professionals but patients who are subjected to cost and time constraints in the diagnosis and treatment processes of heart disease. AbstractHeart disease remains the leading cause of death, such that nearly one-third of all deaths worldwide are estimated to be caused by heart-related conditions. Advancing applications of classification-based machine learning to medicine facilitates earlier detection. In this study, the Classification and Regression Tree (CART) algorithm, a supervised machine learning method, has been employed to predict heart disease and extract decision rules in clarifying relationships between input and output variables. In addition, the study’s findings rank the features influencing heart disease based on importance. When considering all performance parameters, the 87% accuracy of the prediction validates the model’s reliability. On the other hand, extracted decision rules reported in the study can simplify the use of clinical purposes without needing additional knowledge. Overall, the proposed algorithm can support not only healthcare professionals but patients who are subjected to cost and time constraints in the diagnosis and treatment processes of heart disease. Heart disease remains the leading cause of death, such that nearly one-third of all deaths worldwide are estimated to be caused by heart-related conditions. Advancing applications of classification-based machine learning to medicine facilitates earlier detection. In this study, the Classification and Regression Tree (CART) algorithm, a supervised machine learning method, has been employed to predict heart disease and extract decision rules in clarifying relationships between input and output variables. In addition, the study’s findings rank the features influencing heart disease based on importance. When considering all performance parameters, the 87% accuracy of the prediction validates the model’s reliability. On the other hand, extracted decision rules reported in the study can simplify the use of clinical purposes without needing additional knowledge. Overall, the proposed algorithm can support not only healthcare professionals but patients who are subjected to cost and time constraints in the diagnosis and treatment processes of heart disease. •We employ a decision tree algorithm to model and predict heart disease.•We build and train the decision model using electronic health records data of 1190 patients.•On the basis of patients’ characteristics and using decision tree analysis, IF–THEN rules are extracted.•The importance of features in the decision tree analysis has been investigated.•The Classification and Regression Tree (CART) algorithm performs reasonably well in predicting heart disease. |
| ArticleNumber | 100130 |
| Author | Ozcan, Mert Peker, Serhat |
| Author_xml | – sequence: 1 givenname: Mert surname: Ozcan fullname: Ozcan, Mert email: ozcanmert87@gmail.com – sequence: 2 givenname: Serhat surname: Peker fullname: Peker, Serhat email: serhat.peker@bakircay.edu.tr |
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| Keywords | Data mining Decision trees Predictive analytics Classification and regression trees Machine learning Decision rule |
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| SubjectTerms | Classification and regression trees Data mining Decision rule Decision trees Health Systems Science Machine learning Predictive analytics Public Health |
| Title | A classification and regression tree algorithm for heart disease modeling and prediction |
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