Heart Attack Prediction Model Based on Feature Selection and Decision Tree Approaches

Hussein Abdullah Jaber, Mortada Sadoun Thabet, Rabab Abdul Hussein Fahd, Dalal Khatib Muhbis, Alaa Khalaf Hamoud


The purpose of this study is creating a machine learning based model is to predict heart attacks is to improve the capacity to anticipate the occurrence of this dangerous medical condition. It is feasible to find significant and linked variables that may cause heart attacks by using the decision tree as a tool for medical data analysis. The system analyzes clinical data using artificial intelligence techniques to find patterns that might suggest the possibility of a heart attack. The advantage is early disease detection and prediction, which allows the medical staff to better plan treatment and take preventative action. This kind of system can aid in enhancing patient care and lowering the likelihood that. Throuought the study, two paths will be examined, the first one is applying machine learning algorithms without applying feature selection, and the second one with feature selection process. Three mainly feature selection algorithms will be examined to find the most correlated features that affect the heart attack. The model will examine six machine learning decision tree algorithms namely (decision stump, hoeffding tree, j48, LMT, random forest, and rep tree) to find the accurate algorithm in prediction. The results show that LMT have the accurate prediction accuracy with 82.5%.


Heart Attack; Feature Selection; Machine Learning; Decision Tree; LMT; Weka.

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DOI: http://dx.doi.org/10.21533/scjournal.v13i1.278


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Copyright (c) 2024 Hussein Abdullah Jaber, Mortada Sadoun Thabet, Rabab Abdul Hussein Fahd, Dalal Khatib Muhbis, Alaa Khalaf Hamoud

ISSN 2233 -1859

Digital Object Identifier DOI: 10.21533/scjournal

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