Explainable Heart Disease Diagnosis with Supervised Learning Methods
Tsehay Admassu Assegie,
S. J. Sushma,
Shonazarova Shakhnoza Mamanazarovna
Abstract:The objective of this study is to develop a heart disease diagnosis model with a supervised machine learning algorithm. To that end, random forest (RF), support vector machine (SVM), Naïve Bayes (NB), and extreme boosting (XGBoost) are employed in a medical heart disease dataset to develop a model for heart disease prediction. The performance of the algorithms is investigated and compared for automation of heart disease diagnosis. The best model is selected, and a grid search is applied to improve model perfor… Show more
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