Tabular deep neural networks and causal inference-based feature selection for PCOS diagnosis

Abstract

Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder with complex diagnostic challenges due to symptom heterogeneity and limited clinical criteria. This study presents an AI-driven diagnostic framework integrating tabular deep learning, causal inference, and interpretability techniques to enhance PCOS diagnosis. To the best of our knowledge, this is the first study to jointly leverage state-of-the-art tabular models—TabNet, FT-Transformer, and TabPFN—alongside heterogeneous ensemble methods in the context of PCOS diagnosis. Our approach achieves competitive performance, with the Bayesian Neural Network (BNN) attaining 93.83% accuracy on the test set and demonstrating better probability calibration (ECE = 0.02). Key contributions include a Causal-Neural Boosted Ensemble (CNBE) trained on causally significant features via a Causal-Gated Structural Regularization (CGSR) mechanism, and few-shot learning strategies for low-data adaptability. Interpretability is ensured through SHAP analysis and intrinsic attention mechanisms. Causal inference reveals clinically actionable insights, such as the strong causal influence of metabolic and lifestyle factors. The framework contributes to Trustworthy ML principles by providing transparency and uncertainty quantification, offering a foundation for future clinical decision support.