<![CDATA[Polycystic Ovary Syndrome (PCOS) is one of the most common hormonal disorders experienced by women of reproductive age and can lead to various health problems, including menstrual irregularities, infertility, and an increased risk of metabolic diseases. Early detection of PCOS is essential to minimize long-term impacts and improve the quality of life for patients. This study aims to identify effective data preprocessing strategies to enhance the performance of classification models for PCOS detection. The dataset used is open source, consisting of 541 participants with 45 clinical and laboratory features. The main challenges encountered include the presence of many missing values, an imbalanced target class distribution, and a large number of independent features. To address these issues, a series of preprocessing steps were applied, including missing value imputation, data balancing using the Synthetic Minority Over-sampling Technique (SMOTE), and dimensionality reduction using Principal Component Analysis (PCA). A classification model was built using the Random Forest algorithm, and its performance was compared before and after applying PCA. The evaluation results show that before PCA, the model achieved an accuracy of 87.5%, precision of 86%, recall of 86%, and an F1-score of 86%. After applying PCA, performance improved to an accuracy of 90%, precision of 89%, recall of 89%, and an F1-score of 89%. These findings indicate that the right combination of preprocessing strategies, particularly SMOTE and PCA, can significantly improve the efficiency and effectiveness of models in detecting PCOS, thereby supporting the development of more reliable medical decision support systems.]]>
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