<![CDATA[Polycystic Ovary Syndrome (PCOS) represents a multifaceted endocrine–metabolic condition that poses a significant risk to reproductive health in women of childbearing age. The disorder is influenced by various contributing factors and is commonly associated with clinical features such as disrupted ovulation, hormonal imbalance due to excess androgens, and morphological changes in the ovaries. In automated PCOS classification, a major limitation arises from the disproportionate distribution of data samples, in which instances without PCOS considerably outnumber affected cases. This imbalance tends to bias predictive models toward the dominant class, thereby reducing the detection capability for minority instances and increasing the likelihood of missed PCOS diagnoses. To address this issue, this study proposes the incorporation of a Weighted Loss Function into a Neural Network-based classification framework aimed at improving sensitivity to PCOS cases. The research workflow comprises data preprocessing, neural network architecture construction, integration of class-weighted loss, and systematic experimentation across multiple architectural designs and training configurations. The experimental findings demonstrate that applying a Weighted Loss Function with manually assigned class weights of 1:2, a learning rate of 0.001, five hidden layers, and 50 training epochs delivers optimal classification performance. Under these settings, the model achieves high values across evaluation metrics, including precision, recall, F1-score, and overall accuracy, reaching up to 99%. The results confirm that the proposed approach effectively mitigates majority-class bias and enhances the model’s ability to identify PCOS cases. This improvement is further reinforced through careful hyperparameter tuning and comprehensive experimental evaluation.]]>
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