Comparative Analysis of PCOS Classification Using Random Forest: Integration of Mutual Information, SMOTE-Tomek, and Outlier Handling
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Abstract
Polycystic Ovary Syndrome (PCOS) is a hormonal disorder affecting women of reproductive age, with a global prevalence rate of 8–13%. However, approximately 70% of cases remain undiagnosed. This study aimed to develop and compare eight Random Forest classification models for PCOS detection using a publicly available Kaggle dataset. The methodology incorporated three key preprocessing techniques: outlier handling using the Interquartile Range (IQR) method, feature selection through Mutual Information, and class imbalance via SMOTE-Tomek. The results revealed that the best-performing model, which applied outlier removal and SMOTE without feature selection, achieved an accuracy of 94.11%. This result significantly outperformed the baseline Random Forest model, which achieved an accuracy of 87.27% without the application of any preprocessing techniques, such as outlier removal, SMOTE, or feature selection. Moreover, the model utilizing only SMOTE for class balancing achieved an accuracy of 93.84%, underscoring the importance of addressing class imbalance in enhancing classification performance. Notably, feature selection did not consistently improve accuracy, as Random Forest inherently handles feature redundancy, capturing complex feature interactions. These findings highlight the importance of tailored preprocessing strategies, particularly outlier handling and class balancing, for optimizing medical data classification. Future research should explore clinically informed feature selection techniques and assess the generalizability of these findings across diverse datasets to enhance the clinical relevance of PCOS detection models.
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