Data Pre-Processing And Feature Selection Techniques Backward Elimination For Naïve Bayes Classification On Heart Disease Detection - Julius Warih Angkasa; Edi Noersasongk; Purwanto (Jurnal Ekonomi Teknologi dan Bisnis (JETBIS), 2023)
According to a study published in the International Journal of Cardiology titled "Heart failure across Asia: Same healthcare burden but differences in organization of care," the mortality rate due to heart failure in Indonesia is relatively high. The research findings indicate that approximately 5% of the total population in Indonesia suffers from heart failure. Heart disease is a condition that occurs when the heart experiences disruptions, either due to infections or congenital abnormalities. It is important to pay attention to heart disease in order to reduce the mortality rate. However, there are several inaccuracies in identifying heart disease, and it is necessary to perform calculations using a predictive approach utilizing data mining techniques. One of the data mining methods used is the Naïve Bayes (NB) algorithm, which serves as a classification technique. Additionally, before performing the classification, issues with the data content are often encountered, such as the presence of missing values. This problem can interfere with the classification process; therefore, a special technique called pre-processing is needed to remove missing values. By employing this technique, it can support obtaining accurate prediction results. Furthermore, to support the classification, this study applies feature selection using the Backward Elimination (BE) method to enhance accuracy. In this study, through the implementation of data pre-processing techniques and feature selection, the accuracy rate was successfully improved to 98.31%.