Optimizing Machine Learning for Diabetes Detection: Addressing Class Imbalance with SMOTE and Random Forest Ensemble Learning

Optimizing Machine Learning for Diabetes Detection: Addressing Class Imbalance with SMOTE and Random Forest Ensemble Learning | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 25 February 2025 V1 Latest version Share on Optimizing Machine Learning for Diabetes Detection: Addressing Class Imbalance with SMOTE and Random Forest Ensemble Learning Authors : Kotadi Chinnaiah­ 0000-0002-7382-9931 [email protected] , Devidas Kanchetti , and Rajesh Munirathnam Authors Info & Affiliations https://doi.org/10.22541/au.174048501.13296000/v1 279 views 113 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Diabetes is a widespread metabolic disorder with serious health consequences including cardiovascular disease, kidney failure, and neuropathy. An early and precise diagnosis is crucial for effective disease management. However, conventional diagnostic methods, such as fasting blood glucose (FBG) and oral glucose tolerance tests (OGTT), are resource-intensive and impractical for large-scale screening, particularly in underserved areas. Machine learning (ML) techniques have shown great promise in predictive healthcare; however, their performance is often compromised by class imbalance in medical datasets, leading to biased models and suboptimal detection of diabetic cases.To address this challenge, this study explores the integration of the Synthetic Minority Over-sampling Technique (SMOTE) with a Random Forest (RF) classifier to enhance diabetes prediction. SMOTE generates synthetic samples to balance the dataset, whereas Random Forest is an ensemble learning method that constructs multiple decision trees to improve model robustness and accuracy. A comparative evaluation of various ML models, including Logistic Regression, K-Nearest Neighbors (KNN), Decision Trees, Bagging Classifiers, XGBoost, and CatBoost, was conducted using a diabetes dataset. Among these, the SMOTE-enhanced Random Forest model demonstrated superior performance, achieving the highest recall (0.875) and F1-score (0.843) and significantly improving diabetic patient identification. The innovation of this approach lies in the strategic fusion of data augmentation and ensemble learning, which effectively mitigates class imbalance and enhances predictive reliability. This framework minimizes false negatives, ensuring that more at-risk individuals are detected accurately. The findings highlight the potential of AI-driven healthcare solutions to bridge the gap between traditional diagnostics and automated early detection, particularly in resource-constrained environments. The proposed model offers a scalable, cost-effective, and interpretable solution for improving diabetes screening, and contributes to the advancement of AI-powered preventive medicine. Supplementary Material File (diabeticsn.docx) Download 1.18 MB Information & Authors Information Version history V1 Version 1 25 February 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords class imbalance diabetes prediction machine learning random forest classifier smote Authors Affiliations Kotadi Chinnaiah­ 0000-0002-7382-9931 [email protected] G H Raisoni College of Engineering View all articles by this author Devidas Kanchetti G H Raisoni College of Engineering View all articles by this author Rajesh Munirathnam G H Raisoni College of Engineering View all articles by this author Metrics & Citations Metrics Article Usage 279 views 113 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Kotadi Chinnaiah­, Devidas Kanchetti, Rajesh Munirathnam. Optimizing Machine Learning for Diabetes Detection: Addressing Class Imbalance with SMOTE and Random Forest Ensemble Learning. Authorea . 25 February 2025. 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