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

• Autorzy: Krishna Munagala N.V.L.M. , Langoju Lakshmi Rajeswara Rao , Rani A. Daisy , Reddy D.V. Rama Koti

Identyfikatory

DOI

10.1016/j.bbe.2022.10.001

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

IoT; heart disease; monitoring system; optimal feature selection; LSTM; Harris hawks optimization

PL

IoT; choroba serca; system monitorowania; wybór funkcji; LSTM

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2022
• Tom: Vol. 42, no. 4
• Strony: 1183--1204
• Opis fizyczny: Bibliogr. 56 poz., rys., tab., wykr.

Twórcy

autor

Krishna Munagala N.V.L.M.

• Department of Electrical Electronics and Communication Engineering, GITAM Deemed to be University, Visakhapatnam, India

autor

Langoju Lakshmi Rajeswara Rao

• Department of Electrical Electronics and Communication Engineering, GITAM Deemed to be University, Visakhapatnam, India

autor

Rani A. Daisy

• Department of Instrument Technology AU College of Engineering, Andhra University, Visakhapatnam, India

autor

Reddy D.V. Rama Koti

• Department of Instrument Technology AU College of Engineering, Andhra University, Visakhapatnam, India

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)