Integration of IoT and Machine Learning for Real-Time Monitoring and Control of Heart Disease Patients

first_page Download PDF settings Order Article Reprints Font Type: Arial Georgia Verdana Font Size: Aa Aa Aa Line Spacing:    Column Width:    Background: Open AccessAbstract Integration of IoT and Machine Learning for Real-Time Monitoring and Control of Heart Disease Patients † by Neelamadhab PadhyNeelamadhab Padhy SciProfiles Scilit Preprints.org Google Scholar *, Rasmita PanigrahiRasmita Panigrahi SciProfiles Scilit Preprints.org Google Scholar , Sibo Prasad PatroSibo Prasad Patro SciProfiles Scilit Preprints.org Google Scholar , Vishal Kumar SwainVishal Kumar Swain SciProfiles Scilit Preprints.org Google Scholar and Kiran Kumar SahuKiran Kumar Sahu SciProfiles Scilit Preprints.org Google Scholar Department of Computer Science and Engineering, School of Engineering and Technology, GIET University, Gunupur 765022, Odisha, India * Author to whom correspondence should be addressed. † Presented at the 3rd International Electronic Conference on Processes—Green and Sustainable Process Engineering and Process Systems Engineering (ECP 2024), 29–31 May 2024; Available online: https://sciforum.net/event/ECP2024. Proceedings 2024, 105(1), 32; https://doi.org/10.3390/proceedings2024105032 Published: 28 May 2024 (This article belongs to the Proceedings of The 3rd International Electronic Conference on Processes—Green and Sustainable Process Engineering and Process Systems Engineering) Download keyboard_arrow_down Download PDF Download PDF with Cover Download XML Download Epub Versions Notes Keywords: heart disease; AI; deep learning; IoT; performance parameter (accuracy, precision, recall) Context: In the 21st century, the integration of IoT and AI plays a vital role in the real-time monitoring and control of heart disease. As per the records, cardiovascular diseases persist as a significant global health challenge, impacting the lives of over half a billion individuals worldwide.Objective: The main objective of this paper is to predict heart disease using deep learning techniques.Materials/Methods: We have considered the performance metrics of deep learning algorithms (Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP), and Convolutional Neural Networks (CNNs)) achieving accurate and efficient monitoring outcomes through accuracy, precision, recall, and F-measure. We have proposed one model that uses a deep learning algorithm.Results: Our experimental result reveals that the deep learning algorithm CNN outperforms in comparison to other algorithms and it has achieved 96% accuracy. Another algorithm, ANN, achieved 92% accuracy indicating a balanced precision–recall tradeoff. We further compared our work with the state of the art, and CNN provides a promising result.Comparison of the proposed work with existing state-of-the-art approaches.Conclusions: We have collected the IoT sensory data from different patients and integrated them with the machine learning algorithms for real-time monitoring and control for heart disease patients. Our integration approach reveals that CNN is the best classifier that handles multidimensional data Author ContributionsConceptualization, N.P. and S.P.P.; methodology, R.P.; software, K.K.S.; validation, N.P. and V.K.S.; formal analysis, N.P.; investigation, R.P.; resources, R.P.; data curation, S.P.P.; writing—original draft preparation, N.P.; writing—review and editing, N.P.; visualization, K.K.S. All authors have read and agreed to the published version of the manuscript.FundingThis research received no external funding.Institutional Review Board StatementNot applicable.Informed Consent StatementNot applicable.Data Availability StatementData is available in the Kaggale repository.Conflicts of InterestThe authors declare no conflict of interest.Disclaimer/Publisher's Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Share and Cite MDPI and ACS Style Padhy, N.; Panigrahi, R.; Patro, S.P.; Swain, V.K.; Sahu, K.K. Integration of IoT and Machine Learning for Real-Time Monitoring and Control of Heart Disease Patients. Proceedings 2024, 105, 32. https://doi.org/10.3390/proceedings2024105032 AMA Style Padhy N, Panigrahi R, Patro SP, Swain VK, Sahu KK. Integration of IoT and Machine Learning for Real-Time Monitoring and Control of Heart Disease Patients. Proceedings. 2024; 105(1):32. https://doi.org/10.3390/proceedings2024105032 Chicago/Turabian Style Padhy, Neelamadhab, Rasmita Panigrahi, Sibo Prasad Patro, Vishal Kumar Swain, and Kiran Kumar Sahu. 2024. "Integration of IoT and Machine Learning for Real-Time Monitoring and Control of Heart Disease Patients" Proceedings 105, no. 1: 32. https://doi.org/10.3390/proceedings2024105032 Article Metrics No No Article Access Statistics Multiple requests from the same IP address are counted as one view.