SISTEM PENDUKUNG KEPUTUSAN PENANGANAN PENDERITA JANTUNG MENGGUNAKAN KLASIFIKASI NAÏVE BAYES

Abstract

According to the official website of Indonesia’s Ministry of Health (Kemenkes RI) in 2023, cardiovascular disease is the leading cause of death worldwide. This illness often affects individuals in their productive years, causing a high mortality rate that leads to significant social and economic burdens. The American Heart Association reported that over 17 million deaths are caused by cardiovascular disease annually, and this number is projected to increase to 23.3 million by 2030.Medical diagnosis of heart disease still faces various challenges, particularly due to incomplete or reduced data. To address this issue, computer-based data mining techniques can help uncover meaningful insights from heart disease patient datasets. One commonly used approach in data mining is classification, which involves building models to differentiate between data classes and predict unknown class labels.This study applies the Naïve Bayes classification algorithm to identify heart disease risks. Using a dataset from Kaggle, the research involves eight independent variables and one dependent variable to classify patient data. The aim is not only to demonstrate the algorithm’s usefulness but also to educate both medical professionals and the wider community.As part of the implementation, a web-based application is developed, enabling easier access to early and independent heart disease screening. With widespread internet availability, this tool has the potential to assist in early diagnosis and improve public awareness about heart health