An Optimized Ensemble Learning Framework using Boosted Random Subspace SVMs and Stacked Generalization for Cardiovascular Disease Diagnosis

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 6

Year of Publication : 2025

Authors : J. Raghunath, S. Kiran

10.14445/23488549/IJECE-V12I6P126

How to Cite?

J. Raghunath, S. Kiran, "An Optimized Ensemble Learning Framework using Boosted Random Subspace SVMs and Stacked Generalization for Cardiovascular Disease Diagnosis," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 6, pp. 326-339, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I6P126

Abstract:

Cardiovascular disease causes most deaths worldwide, so health organizations seek to produce dependable automated medical diagnosis tools. Our proposed method combines a Support Vector Machine with boosted random subspace and stacked generalization to make more accurate CVD diagnosis predictions. The framework begins by normalizing data as part of preprocessing to normalize feature scales from clinical data. Several SVM-based learners gain training via the diverse feature subsets that the Random Subspace Method (RSM) generates. The learners achieve optimized kernel parameters by performing a grid search optimization. The voting scheme in bootstrap aggregation methods improves diversity while controlling overfitting to generate predictions. The model generalization requires stacked generalization that integrates base learner outputs into a second-level logistic regression prediction system. The assessment method involves checking accuracy rates together with precision values and recall rates and includes F1-score and Area Under the Receiver Operating Characteristics Curve (AUC) measurements. Experimental benchmark results validate that the ensemble model reaches an accuracy rate of 96.39%, surpassing standard single classifiers together with standard ensemble techniques in predicting heart disease, thus proving its clinical value for cardiovascular assistance. The proposed diagnostic framework demonstrates strength and expandability for medical diagnosis procedures that require outstanding interpretive capabilities along with specific prediction accuracy.

Keywords:

Accuracy, Boosted SVM, Cardio Vascular Disease, F1-score, Precision, Random subspace, Recall.

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