Open AccessAn Intelligent System for Heart Disease Prediction using Adaptive Neuro-Fuzzy Inference Systems and Genetic Algorithm
Author(s) -
J. Feng,
Qian Wang,
Na Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2010/1/012172
Subject(s) - adaptive neuro fuzzy inference system , computer science , inference system , inference , machine learning , fuzzy inference system , set (abstract data type) , artificial intelligence , disease , soft computing , fuzzy logic , fuzzy control system , heart disease , genetic algorithm , data mining , algorithm , medicine , pathology , programming language
Cardiovascular disease remains the leading cause of death worldwide over the past two decades. Because of a large number of clinical data and the complexity of the disease, it is often challenging to diagnose and make the proper treatment. Over the past decade, as a soft computing method, fuzzy expert systems have been applied in disease diagnosis by many researches because of its superiority in dealing with uncertain and ambiguous problems. This study proposes an Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to diagnose heart disease. Parameters related to membership functions in ANFIS are optimized by applying the genetic algorithm. The experiment was conducted on the public UCI heart disease datasets. The experimental result shows that 91.25% accuracy was obtained on the testing set, which was found to be satisfying based on comparison.