Novel Internet of Things based approach toward diabetes prediction using deep learning models.

Autor:	Naseem A; Faculty of Computer Sciences, Ibadat International University, Islamabad, Pakistan., Habib R; Faculty of Computer Sciences, Ibadat International University, Islamabad, Pakistan., Naz T; CMAC Future Manufacturing Research Hub, University of Strathclyde, Glasgow, United Kingdom., Atif M; Department of Computer Science and IT, The University of Lahore, Lahore, Pakistan., Arif M; Department of Computer Science and IT, The University of Lahore, Lahore, Pakistan., Allaoua Chelloug S; Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Jazyk:	angličtina
Zdroj:	Frontiers in public health [Front Public Health] 2022 Aug 24; Vol. 10, pp. 914106. Date of Electronic Publication: 2022 Aug 24 (Print Publication: 2022).
DOI:	10.3389/fpubh.2022.914106
Abstrakt:	The integration of the Internet of Things with machine learning in different disciplines has benefited from recent technological advancements. In medical IoT, the fusion of these two disciplines can be extremely beneficial as it allows the creation of a receptive and interconnected environment and offers a variety of services to medical professionals and patients. Doctors can make early decisions to save a patient's life when disease forecasts are made early. IoT sensor captures the data from the patients, and machine learning techniques are used to analyze the data and predict the presence of the fatal disease i.e., diabetes. The goal of this research is to make a smart patient's health monitoring system based on machine learning that helps to detect the presence of a chronic disease in patient early and accurately. For the implementation, the diabetic dataset has been used. In order to detect the presence of the fatal disease, six different machine learning techniques are used i.e., Support Vector Machine (SVM), Logistic Regression, Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). The performance of the proposed model is evaluated by using four evaluation metrics i.e., accuracy, precision, recall, and F1-Score. The RNN outperformed remaining algorithms in terms of accuracy (81%), precision (75%), and F1-Score (65%). However, the recall (56%) for ANN was higher as compared to SVM and logistic regression, CNN, RNN, and LSTM. With the help of this proposed patient's health monitoring system, doctors will be able to diagnose the presence of the disease earlier. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Naseem, Habib, Naz, Atif, Arif and Allaoua Chelloug.)
Databáze:	MEDLINE
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