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All Journal Advance Sustainable Science, Engineering and Technology (ASSET)
K. Jegadeeswari
Periyar University
Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Optimized Stacking Ensemble Classifier for Early Cancer Detection Using Biomarker Data K. Jegadeeswari; R. Rathipriya
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.986

Abstract

Ovarian cancer ranks sixth globally as a major cause of death among women, with a five-year survival rate below 50%, largely due to late detection. Early detection is crucial to lower mortality rates. This paper introduces an Optimized Stacking Ensemble Classifier (OSEC) for early ovarian cancer detection using biomarkers. The model comprises two layers: the first layer includes base classifiers optimized with Particle Swarm Optimization (PSO), while the second layer is a meta-classifier integrating Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest(RF) models fine-tuned through grid search. Among the three datasets evaluated, the Blood Routine dataset showed the best performance with a stacked RF meta-classifier, achieving: 94.29% accuracy. The Stacked RF model also outperformed others, reaching 92.82% accuracy on the Serum dataset and 92.77% on the Malignant Ovarian Tumor (MOT) dataset, consistently excelling in precision, recall, and f1-score.
Co-Authors R. Rathipriya