ArticleBioengineering (Basel, Switzerland)2024
Leveraging Shapley Additive Explanations for Feature Selection in Ensemble Models for Diabetes Prediction.
Article in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Explainable Machine Learning Models for Predicting Functional and Pain Recovery After Fragility Fracture Surgery.Diagnostics (Basel, Switzerland) · 2026Article
- An Optimized and Explainable Machine Learning Framework for Diabetes Prediction Using Marine Predators Algorithm and SHAP.Journal of personalized medicine · 2026Article
- Development and Validation of Artificial Intelligence Prediction of Epicardial Coronary Artery Spasm in Patients Without Obstructive Coronary Artery Disease.Diagnostics (Basel, Switzerland) · 2026Article
- An interpretable progressive residual network for automated multiclass diabetes diagnosis.Scientific reports · 2026Article
- A leakage-controlled and SHAP driven machine learning framework for paediatric respiratory disease classification using Indian hospital EHR data.BMC medical informatics and decision making · 2026Article
- Prediction of Five-Year Mortality Risk of Chronic Kidney Disease Using Artificial Intelligence-Based Models: A Retrospective Study.Health science reports · 2026Article
- What makes a scent trigger a memory? A cognitive decomposition of odor-evoked retrieval.iScience · 2026Article
- Development, validation, and visualization of a machine learning-based predictive model for sarcopenia risk in patients with diabetes mellitus.Frontiers in medicine · 2026Article
- FaXNet: a frequency-adaptive, explainable, and uncertainty-aware network for influenza forecasting.Frontiers in public health · 2026Article
- Machine Learning-Based Identification of Patients with Elevated Central Venous Pressure Using Features Extracted from Photoplethysmography Waveforms.medRxiv : the preprint server for health sciences · 2025Article
- Construction and validation of the prediction model for kinesiophobia in older adults with chronic low back pain.BMC geriatrics · 2025Article
- Article
- Small dense LDL: An underestimated driver of atherosclerosis (Review).Molecular medicine reports · 2025Review
- An Effective Methodology for Diabetes Prediction in the Case of Class Imbalance.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- Machine learning models for predicting extended length of stay and hospital charges in nontraumatic subarachnoid hemorrhage.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetes, a significant global health crisis, is primarily driven in India by unhealthy diets and sedentary lifestyles, with rapid urbanization amplifying these effects through convenience-oriented living and limited physical activity opportunities, underscoring the need for advanced preventative strategies and technology for effective management. This study integrates Shapley Additive explanations (SHAPs) into ensemble machine learning models to improve the accuracy and efficiency of diabetes predictions. By identifying the most influential features using SHAP, this study examined their role in maintaining high predictive performance while minimizing computational demands. The impact of feature selection on model accuracy was assessed across ten models using three feature sets: all features, the top three influential features, and all except these top three. Models focusing on the top three features achieved superior performance, with the ensemble model attaining a better performance in most of the metrics, outperforming comparable approaches. Notably, excluding these features led to a significant decline in performance, reinforcing their critical influence. These findings validate the effectiveness of targeted feature selection for efficient and robust clinical applications.
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Registered trials
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 Socratic graph.