Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications

Author:

Butt Umair Muneer1ORCID,Letchmunan Sukumar1ORCID,Ali Mubashir2ORCID,Hassan Fadratul Hafinaz1ORCID,Baqir Anees3ORCID,Sherazi Hafiz Husnain Raza4ORCID

Affiliation:

1. School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia

2. Department of Management,Information and Production Engineering, University of Bergamo, Bergamo, Italy

3. Department of Environmental Sciences,Informatics,and Statistics, Ca’Foscari University of Venice, Venice, Italy

4. School of Computing and Engineering, University of West London, London, UK

Abstract

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.

Funder

Universiti Sains Malaysia

Publisher

Hindawi Limited

Subject

Health Informatics,Biomedical Engineering,Surgery,Biotechnology

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