OPTIMIZATION OF NAÏVE BAYES USING BACKWARD ELIMINATION FOR HEART DISEASE DETECTION - Amri, Saeful; Ningrum, Ariska Fitriyana; Arum, Prizka Rismawati (Department Statistics, Faculty Mathematics and Natural Science, UNIMUS, 2023)
Heart disease is the main cause of death in humans. Even though preventive measures have been taken such as regulating food (diet), lowering cholesterol, and treating weight, diabetes, and hypertension, heart disease remains a major health problem. There are several factors that cause heart disease, including age, type of chest pain, high blood pressure, sugar levels, ECG test values, maximum heart rate, and induced angina. To reduce the percentage of deaths due to heart disease, we need a system that can predict heart disease. The algorithm used in this research is a combination of the Backward Elimination and Naive Bayes algorithms to increase accuracy in diagnosing heart disease. According to the results of this research, the Naive Bayes algorithm has an accuracy value of 78.90% and an Area Under Curve (AUC) value of 0.86, which is included in the good classification category. Combining the Backward Elimination and Naïve Bayes algorithms has an accuracy value of 82.31% and an Area Under Curve (AUC) value of 0.88.