ArticleNeural computing & applications2022
Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms.
Article in Neural computing & applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
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Who cites it
47 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis.Frontiers in digital health · 2025Pooled it
- Decision tree with randomized grid search-based hyperparameter tuning and optimal feature scaling for diabetes diagnosis.BMC bioinformatics · 2026Article
- Advanced CNN Deep Learning Model for Diabetic Retinopathy Classification.Journal of biomedical physics & engineering · 2026Article
- An Interpretable Fuzzy Distance-Based Ensemble Framework with SHAP Analysis for Clinically Transparent Prediction of Diabetes.Diagnostics (Basel, Switzerland) · 2026Article
- Feature extraction in sensor plant disease datasets using reformed membership functions independent of class variables.Scientific reports · 2026Article
- Privacy-Preserving Collaborative Diabetes Prediction in Heterogeneous Health Care Systems: Algorithm Development and Validation of a Secure Federated Ensemble Framework.JMIR diabetes · 2026Article
- Large Models for Small Tables: Adapting Tabular Foundation Models to EHR Data.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Flexible statistical approaches for modeling nonlinear relationships in diabetes prediction using splines, Bayesian kernel regression and Bayesian regression trees.Discover analytics · 2026Article
- Discovery of Novel 1,3,4-oxadiazole-based Inhibitors Against Urease and Diabetes: Design, Synthesis, SAR, Biological, and Molecular Docking Screening.Medicinal chemistry (Shariqah (United Arab Emirates)) · 2026Article
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- Foundation Model-Guided Synthetic EHR Release: Performance Enhancement with Privacy Preservation.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- An ethnic-sensitive hybrid framework for T2D prediction with explainable AI and weighted ensembles.Scientific reports · 2025Article
- A hybrid bio inspired neural model based on Ropalidia Marginata behavior for multi disease classification.Scientific reports · 2025Article
- Diabetes Prediction Using Feature Selection Algorithms and Boosting-Based Machine Learning Classifiers.Diagnostics (Basel, Switzerland) · 2025Article
- A Robust Semi-Supervised Brain Tumor MRI Classification Network for Data-Constrained Clinical Environments.Diagnostics (Basel, Switzerland) · 2025Article
- From Data to Diagnosis: A Novel Deep Learning Model for Early and Accurate Diabetes Prediction.Healthcare (Basel, Switzerland) · 2025Article
- Generalizability of machine learning models for diabetes detection a study with nordic islet transplant and PIMA datasets.Scientific reports · 2025Article
- A novel RFE-GRU model for diabetes classification using PIMA Indian dataset.Scientific reports · 2025Article
- An Effective Methodology for Diabetes Prediction in the Case of Class Imbalance.Bioengineering (Basel, Switzerland) · 2025Article
- Next-generation diabetes diagnosis and personalized diet-activity management: A hybrid ensemble paradigm.PloS one · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper proposes an e-diagnosis system based on machine learning (ML) algorithms to be implemented on the Internet of Medical Things (IoMT) environment, particularly for diagnosing diabetes mellitus (type 2 diabetes). However, the ML applications tend to be mistrusted because of their inability to show the internal decision-making process, resulting in slow uptake by end-users within certain healthcare sectors. This research delineates the use of three interpretable supervised ML models: Naïve Bayes classifier, random forest classifier, and J48 decision tree models to be trained and tested using the Pima Indians diabetes dataset in R programming language. The performance of each algorithm is analyzed to determine the one with the best accuracy, precision, sensitivity, and specificity. An assessment of the decision process is also made to improve the model. It can be concluded that a Naïve Bayes model works well with a more fine-tuned selection of features for binary classification, while random forest works better with more features.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.