Authors - Sarthak, Utkarsh Kumar Singh, Ankur Yadav, Aarushi Sharma, Samarth Saxena, Vaishnavi Kumari Singh, Anisha Kumari Abstract - Cervical cancer prediction using machine learning is often limited by class imbalance, dataset variability, and insufficient control of false positive rates. While many existing models report high accuracy, they frequently fail to maintain a clinically appropriate balance between sensitivity and specificity, particularly across datasets with different sizes and feature structures [1]. Models trained on large clinical risk-factor datasets may not generalize well to smaller behavioral datasets, and recall-oriented optimization can significantly increase false positives. This study proposes a false positive–optimized ensemble framework combining behavioral and clinical risk factors and analyzes its performance across two heterogeneous datasets. Threshold tuning and ensemble techniques, including soft voting and stacking, are employed to increase minority-class detection while retaining specificity. Results indicate that independent classifiers show dataset-dependent instability, with trade-offs between recall and false positive control. However, ensemble methods provide more consistent accuracy, precision, recall, and F1-score across datasets. The findings demonstrate that threshold optimization combined with ensemble learning improves cross-dataset robustness and supports more clinically reliable cervical cancer prediction.
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