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. 2022 Jul 14:2022:4451792.
doi: 10.1155/2022/4451792. eCollection 2022.

Blended Ensemble Learning Prediction Model for Strengthening Diagnosis and Treatment of Chronic Diabetes Disease

T R Mahesh  1 , Dhilip Kumar  2 , V Vinoth Kumar  1 , Junaid Asghar  3 , Banchigize Mekcha Bazezew  4 , Rajesh Natarajan  5 , V Vivek  1
Affiliations

Blended Ensemble Learning Prediction Model for Strengthening Diagnosis and Treatment of Chronic Diabetes Disease

T R Mahesh et al. Comput Intell Neurosci. .

Abstract

Diabetes mellitus (DM), commonly known as diabetes, is a collection of metabolic illnesses characterized by persistently high blood sugar levels. The signs of elevated blood sugar include increased hunger, frequent urination, and increased thirst. If DM is not treated properly, it may lead to several complications. Diabetes is caused by either insufficient insulin production by the pancreas or an insufficient insulin response by the body's cells. Every year, 1.6 million individuals die from this disease. The objective of this research work is to use relevant features to construct a blended ensemble learning (EL)-based forecasting system and find the optimal classifier for comparing clinical outputs. The EL based on Bayesian networks and radial basis functions has been proposed in this article. The performances of five machine learning (ML) techniques, namely, logistic regression (LR), decision tree (DT) classifier, support vector machine (SVM), K-nearest neighbors (KNN), and random forest (RF), are compared with the proposed EL technique. Experiments reveal that the EL method performs better than the existing ML approaches in predicting diabetic illness, with the remarkable accuracy of 97.11%. The proposed ensemble learning could be useful in assisting specialists in accurately diagnosing diabetes and assisting patients in receiving the appropriate therapy.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Diabetes' symptoms.
Figure 2
Figure 2
Type-2 diabetes complications.
Figure 3
Figure 3
Diabetes retinopathy.
Figure 4
Figure 4
Different phases of the prediction process.
Figure 5
Figure 5
A single DT.
Figure 6
Figure 6
RF example.
Figure 7
Figure 7
Proposed process diagram.
Figure 8
Figure 8
Confusion matrix.
Figure 9
Figure 9
Performance of various ML techniques.
Figure 10
Figure 10
Accuracy of algorithms.
Figure 11
Figure 11
Computation analyses of ML techniques.
Algorithm 1
Algorithm 1
Stages in the proposed ensemble learning (EL) model.
See this image and copyright information in PMC

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