Early Prediction of Diabetes Using an Ensemble of Machine Learning Models

Diabetes is one of the most rapidly spreading diseases in the world, resulting in an array of significant complications, including cardiovascular disease, kidney failure, diabetic retinopathy, and neuropathy, among others, which contribute to an increase in morbidity and mortality rate. If diabetes...

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Veröffentlicht in:International journal of environmental research and public health 2022-09, Vol.19 (19), p.12378
Hauptverfasser: Dutta, Aishwariya, Hasan, Md Kamrul, Ahmad, Mohiuddin, Awal, Md Abdul, Islam, Md Akhtarul, Masud, Mehedi, Meshref, Hossam
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container_issue 19
container_start_page 12378
container_title International journal of environmental research and public health
container_volume 19
creator Dutta, Aishwariya
Hasan, Md Kamrul
Ahmad, Mohiuddin
Awal, Md Abdul
Islam, Md Akhtarul
Masud, Mehedi
Meshref, Hossam
description Diabetes is one of the most rapidly spreading diseases in the world, resulting in an array of significant complications, including cardiovascular disease, kidney failure, diabetic retinopathy, and neuropathy, among others, which contribute to an increase in morbidity and mortality rate. If diabetes is diagnosed at an early stage, its severity and underlying risk factors can be significantly reduced. However, there is a shortage of labeled data and the occurrence of outliers or data missingness in clinical datasets that are reliable and effective for diabetes prediction, making it a challenging endeavor. Therefore, we introduce a newly labeled diabetes dataset from a South Asian nation (Bangladesh). In addition, we suggest an automated classification pipeline that includes a weighted ensemble of machine learning (ML) classifiers: Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), XGBoost (XGB), and LightGBM (LGB). Grid search hyperparameter optimization is employed to tune the critical hyperparameters of these ML models. Furthermore, missing value imputation, feature selection, and K-fold cross-validation are included in the framework design. A statistical analysis of variance (ANOVA) test reveals that the performance of diabetes prediction significantly improves when the proposed weighted ensemble (DT + RF + XGB + LGB) is executed with the introduced preprocessing, with the highest accuracy of 0.735 and an area under the ROC curve (AUC) of 0.832. In conjunction with the suggested ensemble model, our statistical imputation and RF-based feature selection techniques produced the best results for early diabetes prediction. Moreover, the presented new dataset will contribute to developing and implementing robust ML models for diabetes prediction utilizing population-level data.
doi_str_mv 10.3390/ijerph191912378
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subjects Ablation
Algorithms
Analysis of Variance
Area Under Curve
Bayes Theorem
Bayesian analysis
Cardiovascular diseases
Classification
Complications
Datasets
Diabetes Mellitus
Discriminant analysis
Feature selection
Gestational diabetes
Glucose
Humans
Learning algorithms
Machine Learning
Mathematical models
Medical research
Morbidity
Optimization
Outliers (statistics)
Predictions
Renal failure
Retinopathy
Risk analysis
Risk factors
Statistical analysis
Support vector machines
Variance analysis
Young adults
title Early Prediction of Diabetes Using an Ensemble of Machine Learning Models
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