ArticleHealth information science and systems2022
Machine learning models for classification and identification of significant attributes to detect type 2 diabetes.
Article in Health information science and systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 77 citations in OpenAlex.
- A Systematic Review on Food Recommender Systems for Diabetic Patients.International journal of environmental research and public health · 2023Pooled it
- Bacopa monniera: A Promising Phytotherapeutic Approach in Management of Diabetes-Related Complications.Phytotherapy research : PTR · 2026Review
- An Effective Model-Based Voting Classifier for Diabetes Mellitus Classification.Bioengineering (Basel, Switzerland) · 2026Article
- Hepatotoxicity prediction for traditional Chinese medicine: a two-step in silico framework integrating network and machine learning approaches.BMC complementary medicine and therapies · 2026Article
- AI-driven prediction of insulin resistance in non-diabetic populations using minimal invasive tests: comparing models and criteria.Diabetology & metabolic syndrome · 2025Article
- The study of an integrated diabetes prediction model based on user-defined risk decision making strategy.Medicine · 2025Article
- OptiStack classifier: optimized stacking framework with ensemble feature engineering for enhanced cardiovascular risk prediction.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2025Article
- Can some algorithms of machine learning identify osteoporosis patients after training and testing some clinical information about patients?BMC medical informatics and decision making · 2025Article
- Improved feature reduction framework for sign language recognition using autoencoders and adaptive Grey Wolf Optimization.Scientific reports · 2025Article
- A novel RFE-GRU model for diabetes classification using PIMA Indian dataset.Scientific reports · 2025Article
- Next-generation diabetes diagnosis and personalized diet-activity management: A hybrid ensemble paradigm.PloS one · 2025Article
- Nutrient metabolism and complications of type 2 diabetes mellitus: implications for rehabilitation and precision care.Frontiers in nutrition · 2025Review
- Automated sample annotation for diabetes mellitus in healthcare integrated biobanking.Computational and structural biotechnology journal · 2024Article
- Robust diabetic prediction using ensemble machine learning models with synthetic minority over-sampling technique.Scientific reports · 2024Article
- Enhanced detection of diabetes mellitus using novel ensemble feature engineering approach and machine learning model.Scientific reports · 2024Article
- Combining temporal and spatial attention for seizure prediction.Health information science and systems · 2023Article
- Interrelated feature selection from health surveys using domain knowledge graph.Health information science and systems · 2023Article
- Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance.Diagnostics (Basel, Switzerland) · 2023Article
- Development and validation of an insulin resistance model for a population without diabetes mellitus and its clinical implication: a prospective cohort study.EClinicalMedicine · 2023Article
- Computational design of clinical trials using a combination of simulation and the genetic algorithm.CPT: pharmacometrics & systems pharmacology · 2023Article
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Authors and funding
7 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13755-021-00168-2.
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