Predictive Modeling of Heart Disease with Machine Learning Algorithms

Abstract

Machine learning is recognized as one of the fastest-growing technologies and is widely applied across various fields. The healthcare sector benefits significantly from advances in this technology. In recent years, the prevalence of heart attacks and heart failure has increased across different age groups. Heart failure is a condition in which the heart is unable to deliver sufficient blood to meet the body's metabolic demands and may arise from various underlying causes. A dataset comprising 918 records was analyzed, with missing values addressed, outliers handled, and appropriate preprocessing procedures applied. The dataset was then classified using eleven machine learning algorithms, with feature standardization applied selectively to scale-sensitive models. GridSearchCV was used for hyperparameter optimization, and the models were evaluated using standard classification performance metrics.

Keywords

• Binary classification
• Gridsearchcv function
• Heart disease
• Machine learning

Supporting Institution

Yildiz Technical University

Project Number

FYL-2023-5865

Thanks

This work was supported by the Research Fund of the Yildiz Technical University under project number: FYL-2023-5865. Moreover, first author would like to thank the Scientific and Technological Research Council of Türkiye (TÜBİTAK), Scientist Support Programs Directorate (BİDEB), for providing scholarship support within the scope of the 2210/A National MSc Scholarship Program during her master's studies.

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