Abstract

Purpose: Heart failure is a disease that is still a global threat and plays a major role as the number one cause of death worldwide. Therefore, accurate predictions are needed to determine the survival of heart failure patients. One technique that can be used to predict a decision is classification. Adaptive Neuro-Fuzzy Inference System (ANFIS) is an algorithm that can be used in the classification process in making predictions. Genetic Algorithms can help improve the performance of classification algorithms through the feature selection process. Methods/Study design/approach: In this study, predictions or diagnoses were made on the survival of heart failure patients based on the heart failure clinical record dataset obtained from the UCI Machine Learning Repository. The data used is 299 data with 12 attributes and 1 class. The result of this research is the comparison of the accuracy of the ANFIS algorithm before and after using the Genetic Algorithm. Result/Findings: The ANFIS algorithm produces the highest accuracy of 94.444%. While the ANFIS algorithm after attribute selection using the Genetic Algorithm produces the highest accuracy of 96.667%. This shows that the Genetic Algorithm is able to improve the performance of the ANFIS classification algorithm through the attribute selection process.