Machine learning models for classification and identification of significant attributes to detect type 2 diabetes

Article

Howlader, Koushik Chandra, Satu, Md Shahriare, Awal, Md Abdul, Islam, Md Rabiul, Islam, Sheikh Mohammed Shariful, Quinn, Julian MW and Moni, Mohammad Ali. 2022. "Machine learning models for classification and identification of significant attributes to detect type 2 diabetes." Health Information Science and Systems. 10 (1). https://doi.org/10.1007/s13755-021-00168-2
Article Title

Machine learning models for classification and identification of significant attributes to detect type 2 diabetes

ERA Journal ID212669
Article CategoryArticle
AuthorsHowlader, Koushik Chandra, Satu, Md Shahriare, Awal, Md Abdul, Islam, Md Rabiul, Islam, Sheikh Mohammed Shariful, Quinn, Julian MW and Moni, Mohammad Ali
Journal TitleHealth Information Science and Systems
Journal Citation10 (1)
Article Number2
Number of Pages13
Year2022
PublisherSpringer
Place of PublicationGermany
ISSN2047-2501
Digital Object Identifier (DOI)https://doi.org/10.1007/s13755-021-00168-2
Web Address (URL)https://link.springer.com/article/10.1007/s13755-021-00168-2
Abstract

Type 2 Diabetes (T2D) is a chronic disease characterized by abnormally high blood glucose levels due to insulin resistance and reduced pancreatic insulin production. The challenge of this work is to identify T2D-associated features that can distinguish T2D sub-types for prognosis and treatment purposes. We thus employed machine learning (ML) techniques to categorize T2D patients using data from the Pima Indian Diabetes Dataset from the Kaggle ML repository. After data preprocessing, several feature selection techniques were used to extract feature subsets, and a range of classification techniques were used to analyze these. We then compared the derived classification results to identify the best classifiers by considering accuracy, kappa statistics, area under the receiver operating characteristic (AUROC), sensitivity, specificity, and logarithmic loss (logloss). To evaluate the performance of different classifiers, we investigated their outcomes using the summary statistics with a resampling distribution. Therefore, Generalized Boosted Regression modeling showed the highest accuracy (90.91%), followed by kappa statistics (78.77%) and specificity (85.19%). In addition, Sparse Distance Weighted Discrimination, Generalized Additive Model using LOESS and Boosted Generalized Additive Models also gave the maximum sensitivity (100%), highest AUROC (95.26%) and lowest logarithmic loss (30.98%) respectively. Notably, the Generalized Additive Model using LOESS was the top-ranked algorithm according to non-parametric Friedman testing. Of the features identified by these machine learning models, glucose levels, body mass index, diabetes pedigree function, and age were consistently identified as the best and most frequently accurate outcome predictors. These results indicate the utility of ML methods in constructing improved prediction models for T2D and successfully identified outcome predictors for this Pima Indian population.

KeywordsDiabetes; Classifiers; Feature selection sets; Prediction model; Machine learning models
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020461199. Machine learning not elsewhere classified
Byline AffiliationsNoakhali Science and Technology University, Bangladesh
Khulna University, Bangladesh
University of Wollongong
Deakin University
Garvan Institute of Medical Research, Australia
University of Queensland
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