Citation :

Kalyani Sudhakar Sugarwar, Bhushan Shivram Chaudhari, Anmol Suresh Suryavanshi, Ankush Balaram Pawar, Ashish Ravindra Sonawane, "PSO-Driven Rough Set Attribute Reduction with Transductive SVM for Robust Predictive Healthcare Systems," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 9, pp. 595-608, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I9P141

Abstract

The current paper presents an effective predictive healthcare model that combines Particle Swarm Optimization (PSO) -based Rough Set attribute reduction and a Transductive Support Vector Machine (TSVM) to improve the process of diagnosing diseases in clinical environments. Its aim is to create a generalizable and accurate predictive system that can work with high-dimensional biomedical data, redundant features and fewer labeled data. Six steps comprise the methodology, which entails collecting data based on three benchmark datasets of healthcare-Pima Indians Diabetes, Heart Disease (Cleveland), and Breast Cancer Wisconsin (Diagnostic); systematic preprocessing, which includes imputation, outlier management, normalization, and label encoding; exploratory data analysis, which involves analyzing the data feature distributions and class distribution; PSO-based Rough Set feature reduction, which involves identifying optimal attribute sets; classification using the proposed PSO-RS-TSVM, and performance measurement in terms of accuracy, precision, recall, and The dataset that the proposed model performs best is the Breast Cancer Wisconsin (Diagnostic) dataset; their accuracy of 99.91 is higher than any of the other base methods. In the Heart Disease and Diabetes datasets, the model achieves an accuracy of 99.83 and 99.78, respectively. The findings of this paper indicate the success of using PSO- feature reduction in tandem with transductive learning in obtaining strong, stable, and almost flawless predictions, especially with large, structured biomedical data.

Keywords

Predictive Healthcare, PSO-Driven Rough Set, Transductive Support Vector Machine (TSVM), Feature Reduction and Biomedical Data Classification.

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