Data-driven Clinical Decision Support System Using Neural Network Topology Optimization for PCOS Diagnosis

Polycystic ovarian syndrome (PCOS) is a prevalent endocrine disorder that affects women of reproductive age worldwide.Chronic oligo-or anovulation, clinical hyperandrogenism, and polycystic ovaries characterize it.Long-term complications include endometrial cancer, infertility, obesity, and type 2 diabetes mellitus.Early diagnosis and treatment are crucial to reduce the incidence of these complications.This work aims to develop a Clinical Decision Support System (CDSS) utilizing Multi-Objective Particle Swarm Optimization (MOPSO)backpropagation (BP) with artificial neural networks (ANN) to determine the presence of PCOS.CDSS comprises three subsystems: preprocessing, training, and classification.The preprocessing subsystem manages the missing values and performs correlation-based feature selection with a threshold of 0.25.The training subsystem employs an ANN trained with BP and MOPSO.The local best values are obtained from the BP training seed MOPSO, which has two objective functions: minimizing the mean square error and achieving faster convergence without stagnation.The PCOS dataset from the Kaggle repository is used in the experiments.The developed CDSS achieved an overall accuracy of 92.02%, with 87.27% sensitivity and 94.44% specificity.The CDSS is a valuable second-opinion tool for junior gynecologists in diagnosing PCOS, offering a robust and accurate method for early detection and intervention.