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. 2021 Sep 29:2021:9930985.
doi: 10.1155/2021/9930985. eCollection 2021.

Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications

Umair Muneer Butt  1 , Sukumar Letchmunan  1 , Mubashir Ali  2 , Fadratul Hafinaz Hassan  1 , Anees Baqir  3 , Hafiz Husnain Raza Sherazi  4
Affiliations

Machine Learning Based Diabetes Classification and Prediction for Healthcare Applications

Umair Muneer Butt et al. J Healthc Eng. .

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.

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Conflict of interest statement

The authors declare that there are no conflicts of interest regarding the publication of this article.

Figures

Figure 1
Figure 1
Impact of regular exercise on metabolism of diabetic patients [48].
Figure 2
Figure 2
Proposed MLP architecture with eight variables as input for diabetes classification.
Figure 3
Figure 3
BG prediction using long short-term memory (LSTM) algorithm.
Figure 4
Figure 4
PIMA data distribution.
Figure 5
Figure 5
Dataset features' distribution visualization.
Figure 6
Figure 6
Performance comparison of classifiers.
Figure 7
Figure 7
Performance comparison of forecasting model.
Figure 8
Figure 8
Proposed diabetes classification method vs. state-of-the-art techniques.
Figure 9
Figure 9
Proposed diabetes prediction method vs. state-of-the-art systems.
Figure 10
Figure 10
The proposed hypothetical architecture of the healthcare monitoring system.
Figure 11
Figure 11
Implementation level details of the proposed hypothetical system.
Algorithm 1
Algorithm 1
Diabetes classification algorithm using MLP for healthcare.
Algorithm 2
Algorithm 2
Diabetes prediction algorithm by exploiting LSTM for healthcare.
See this image and copyright information in PMC

References

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