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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Vydáno v:Healthcare analytics (New York, N.Y.) Ročník 3; s. 100130
Hlavní autoři: Ozcan, Mert, Peker, Serhat
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 01.11.2023
Elsevier
Témata:
Classification and regression trees
Data mining
Decision rule
Decision trees
Health Systems Science
Machine learning
Predictive analytics
Public Health
Data mining
Decision trees
Predictive analytics
Classification and regression trees
Machine learning
Decision rule
ISSN:2772-4425, 2772-4425
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Shrnutí: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.
ISSN:2772-4425
2772-4425
DOI:10.1016/j.health.2022.100130