An efficient clinical support system for heart disease prediction using TANFIS classifier. (26th October 2021)

• Record Type: Journal Article
• Authors: Sekar, Jayachitra; Aruchamy, Prasanth; Sulaima Lebbe Abdul, Haleem; Mohammed, Amin Salih; Khamuruddeen, Shaik
• Is Part Of: Computational intelligence. Volume 38:Number 2(2022)
• Page Start: 610
• Page End: 640
• Publication Date: 2021-10-26
• Subjects: classification -- grasshopper optimization algorithm -- heart disease prediction -- internet of things -- moth flame optimization; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12487
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: In today's world, the advancement of telediagnostic equipment plays an essential role to monitor heart disease. The earlier diagnosis of heart disease proliferates the compatibility of treatment of patients and predominantly provides an expeditious diagnostic recommendation from clinical experts. However, the feature extraction is a major challenge for heart disease prediction where the high dimensional data increases the learning time for existing machine learning classifiers. In this article, a novel efficient Internet of Things‐based tuned adaptive neuro‐fuzzy inference system (TANFIS) classifier has been proposed for accurate prediction of heart disease. Here, the tuning parameters of the proposed TANFIS are optimized through Laplace Gaussian mutation‐based moth flame optimization and grasshopper optimization algorithm. The simulation scenario can be carried out using11 different datasets from the UCI repository. The proposed method obtains an accuracy of 99.76% for heart disease prediction and it has been improved upto 5.4% as compared with existing algorithms.