A smart IoT-enabled heart disease monitoring system using meta-heuristic-based Fuzzy-LSTM model

• Published in: Biocybernetics and biomedical engineering 2022-10, Vol.42 (4), p.1183-1204
• Main Authors: Munagala, N.V.L.M. Krishna, Langoju, Lakshmi Rajeswara Rao, Rani, A. Daisy, Reddy, D.V. Rama Koti
• Format: Article
• Language: English
• Subjects: Fitness-based Harris Hawks Optimization; Improved Fuzzy-Long Short Term Memory; IoT-enabled heart disease monitoring system; Optimal feature selection; Population
• ISSN: 0208-5216
• DOI: 10.1016/j.bbe.2022.10.001

A continuous heart disease monitoring system is one of the significant applications specified by the Internet of Things (IoT). This goal might be achieved by combining sophisticated expert systems with extensive healthcare data on heart diseases. Several machine learning-based methods have recently been proven for predicting and diagnosing cardiac illness. However, these algorithms are unable to manage high-dimensional information due to the lack of a smart framework that can combine several sources to anticipate cardiac illness. The Fuzzy-Long Short Term Memory (LSTM) model is used in this work to present a unique IoT-enabled heart disease prediction method. The benchmark data for the experiment came from public sources and collected via wearable IoT devices. An improved Harris Hawks Optimization (HHO) called Population and Fitness-based HHO (PF-HHO) is utilized to select the best features, with the objective function of correlation maximization within the same class and correlation minimization among different classes. The scientific contributions of the health care monitoring system are depicted here that help to improve heart disease healthcare efficiency and also it can be reducing the death rate in the current world. The important section of this persistent healthcare mode is the real-world monitoring system. The simulation outcomes proved that the recommended approach is more successful at predicting heart illness than existing technologies.