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Lookup NU author(s): Dr Husnain SheraziORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2021 Umair Muneer Butt et al. The remarkable advancements in biotechnology and public healthcare infrastructures have led to a momentous production of critical and sensitive healthcare data. By applying intelligent data analysis techniques, many interesting patterns are identified for the early and onset detection and prevention of several fatal diseases. Diabetes mellitus is an extremely life-threatening disease because it contributes to other lethal diseases, i.e., heart, kidney, and nerve damage. In this paper, a machine learning based approach has been proposed for the classification, early-stage identification, and prediction of diabetes. Furthermore, it also presents an IoT-based hypothetical diabetes monitoring system for a healthy and affected person to monitor his blood glucose (BG) level. For diabetes classification, three different classifiers have been employed, i.e., random forest (RF), multilayer perceptron (MLP), and logistic regression (LR). For predictive analysis, we have employed long short-term memory (LSTM), moving averages (MA), and linear regression (LR). For experimental evaluation, a benchmark PIMA Indian Diabetes dataset is used. During the analysis, it is observed that MLP outperforms other classifiers with 86.08% of accuracy and LSTM improves the significant prediction with 87.26% accuracy of diabetes. Moreover, a comparative analysis of the proposed approach is also performed with existing state-of-the-art techniques, demonstrating the adaptability of the proposed approach in many public healthcare applications.
Author(s): Butt UM, Letchmunan S, Ali M, Hassan FH, Baqir A, Sherazi HHR
Publication type: Article
Publication status: Published
Journal: Journal of Healthcare Engineering
Year: 2021
Volume: 2021
Online publication date: 01/10/2021
Acceptance date: 16/08/2021
Date deposited: 24/05/2024
ISSN (print): 2040-2295
ISSN (electronic): 2040-2309
Publisher: Hindawi Limited
URL: https://doi.org/10.1155/2021/9930985
DOI: 10.1155/2021/9930985
Data Access Statement: The data used to support the findings of this study are included within the article.
PubMed id: 34631003
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