An efficient clinical support system for heart disease prediction using TANFIS classifier

Computational Intelligence - Tập 38 Số 2 - Trang 610-640 - 2022
Jayachitra Sekar1, A. Prasanth2, Sulaima Lebbe Abdul Haleem3, Amin Salih Mohammed4,5, Shaik Khamuruddeen6
1Department of Electronics and Communication Engineering, Karpagam Academy of Higher Education, Coimbatore, India
2Department of Electronics and Communication Engineering, Sri Venkateswara College of Engineering, Sriperumpudur, India
3Department of Information & Communication Technology South Eastern University of Sri Lanka Oluvil Sri Lanka
4Department of Computer Engineering, Lebanese French University, Erbil, Iraq
5Department of Software and Informatics Engineering, Salahaddin University, Erbil, Iraq
6Department of Electronics and Communication Engineering KKR & KSR Institute of Technology and Sciences Guntur India

Tóm tắt

AbstractIn 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.

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Computational Intelligence

Tập 38 Số 2

610-640

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