A proposed model using Naïve Bayes and generalized linear models for early detection of heart attack risk

Oman Somantri (Department of Computer and Business, Politeknik Negeri Cilacap)
Linda Perdana Wanti (Department of Computer and Business, Politeknik Negeri Cilacap)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 33, No 2: February 2024

Timely identification of diseases, particularly heart attacks is crucial for individuals, particularly the elderly, to accurately anticipate the onset of the disease based on symtoms. The objective of this study is to develop a highly accurate model for early detection of heart disease using the Naïve Bayes (NB) and generalized linear model (GLM) techniques. In addition, another concern is the model’s subfar accuracy levels, promting the implementation of measures to optimize it. The suggested approach fot optimization involves the utilization of a genetic algorithm (GA). The research findings indicate that the NB and GLM approaches achive a reasonably high level of accuracy. Specifically, the NB model achieves an accuracy of 82.53%, while the GLM achieves an accuracy of 84.50%. Following optimization, the accuracy levels notably improved, with the NB_M-GA model reaching 85.83% and the GLM_M-GA model achieving 86,48%.