Authors : Aminu Usman Jibril; A. Senthil Kumar

Volume/Issue : Volume 11 - 2026, Issue 7 - July

Google Scholar : https://tinyurl.com/34uzsr7e

Scribd : https://tinyurl.com/ytrezhaf

DOI : https://doi.org/10.38124/ijisrt/26jul1418

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Missing values and significant class imbalance are common characteristics of healthcare datasets which significantly impair predictive model performance and reduce their dependability in clinical decision-making. Creation of reliable and broadly applicable healthcare prediction system depends on addressing this issues As to improve overall data quality, this study suggests an integrated data preparation system that integrates cluster-aware oversampling methods with Generative Adversarial Imputation Networks (GAIN). By using adversarial training to understand intricate underlying data distributions GAIN model are used to estimate missing values while maintaining significant statistical correlations between variables. Simultaneously, hybrid SMOTE-ENN method are used to remove ambiguous and noisy data and efficiently handle class imbalance. Real-world diabetic readmission dataset are used to assess suggested methodology, and show notable gains in data completeness distribution preservation, and prediction performance. Significant improvements in accuracy, recall, and F1-score are revealed by experimental data, suggesting improved capacity to detect high-risk individuals. As compared to traditional methods incorporation of sophisticated preprocessing technique enhances model resilience and generalisation. This results highlight significance of integrating class balancing technique and intelligent imputation into single framework. Overall, study emphasises how important sophisticated preprocessing are to enhancing clinical applicability, robustness and dependability of predictive healthcare analytics system.

Keywords : GAN; GAIN; Data Imputation; SMOTE-ENN; Class Imbalance; Predictive Healthcare Analytics; Diabetes Readmission.

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