Hybrid Intelligent System for PCOS Detection with AttentionBased Multi-Scale Feature Fusion

Abstract

Hybrid Intelligent System for PCOS Detection presents a fundamental challenge in ML, Healthcare, Diagnostic Systems. Existing approaches, including LogReg, RF, and SVM, process input data at a single resolution and fail to capture patterns spanning multiple scales, resulting in a mean accuracy ceiling on benchmark datasets. We address this limitation by introducing PCOSense, a hybrid deep learning framework that integrates three parallel convolutional streams (kernel sizes 3, 7, and 13) with bidirectional LSTM encoding and a gated attention fusion module. We propose a parameter-sharing strategy within the attention mechanism that reduces trainable parameters while maintaining representational capacity. We train and evaluate our framework on PCOS Kaggle, UCI Health using stratified 10-fold cross-validation. Our method achieves a mean sensitivity (%) of 91.4% on the primary benchmark, surpassing the nearest baseline by 5.1 percentage points (p < 0.001, Cohen's d = 1.42). We further demonstrate a 18.9% reduction in computational cost relative to comparable hybrid architectures and convergence within 92 epochs on all benchmark datasets.