Supervised Machine Learning based Ensemble Model for Accurate Prediction of Type 2 Diabetes

Akula, Ramya; Nguyen, Ni; Garibay, Iván

doi:10.1109/southeastcon42311.2019.9020358

Cited by 19 publications

(2 citation statements)

References 10 publications

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“…Considering the literature for Pima data, Akula et al (2019) evaluated the classification performance of data preprocessing, feature selection, and ensemble learning algorithms in their study [33]. However, in this study, this problem was ignored by not mentioning any outlier observations existing in the data set, and the applications were made within the scope of outlier observation.…”

Section: Discussionmentioning

confidence: 99%

A Proposal of New Feature Selection Method Sensitive to Outliers and Correlation

Demirarslan

Süner

2021

Preprint

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In disease diagnosis classification, ensemble learning algorithms enable strong and successful models by training more than one learning function simultaneously. This study aimed to eliminate the irrelevant variable problem with the proposed new feature selection method and compare the ensemble learning algorithms' classification performances after eliminating the problems such as missing observation, classroom noise, and class imbalance that may occur in the disease diagnosis data. According to the findings obtained; In the preprocessed data, it was seen that the classification performance of the algorithms was higher than the raw version of the data. When the algorithms' classification performances for the new proposed advanced t- Score and the old t-Score method were compared, the feature selection made with the proposed method showed statistically higher performance in all data sets and all algorithms compared to the old t-Score method (p = 0.0001).

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Section: Discussionmentioning

confidence: 99%

A Proposal of New Feature Selection Method Sensitive to Outliers and Correlation

Demirarslan

Süner

2021

Preprint

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“…The multilayer perceptron model obtained an accuracy of 77%. Another study (4) proposed a customized ensemble model for diabetes classification using the Practice Fusion dataset. The study selected 17 features and built a weighted average ensemble model of SVM, RF, and gradient boosting classifiers.…”

Section: Introductionmentioning

confidence: 99%

Customized and Automated Machine Learning-Based Models for Diabetes Type 2 Classification

Mohsen

Biswas

Ali

et al. 2022

Studies in Health Technology and Informatics

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This study aims to develop models to accurately classify patients with type 2 diabetes using the Practice Fusion dataset. We use Random Forest (RF), Support Vector Classifier (SVC), AdaBoost classifier, an ensemble model, and automated machine learning (AutoML) model. We compare the performance of all models in a five-fold cross-validation scheme using four evaluation measures. Experimental results demonstrate that the AutoML model outperformed individual and ensemble models in all evaluation measures.

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Machine Learning, Deep Learning and Image Processing for Healthcare: A Crux for Detection and Prediction of Disease

Chhabra

Sharma

2021

Lecture Notes on Data Engineering and Communications Technologies

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Supervised Machine Learning based Ensemble Model for Accurate Prediction of Type 2 Diabetes

Cited by 19 publications

References 10 publications

A Proposal of New Feature Selection Method Sensitive to Outliers and Correlation

A Proposal of New Feature Selection Method Sensitive to Outliers and Correlation

Customized and Automated Machine Learning-Based Models for Diabetes Type 2 Classification

Machine Learning, Deep Learning and Image Processing for Healthcare: A Crux for Detection and Prediction of Disease

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