Diagnosis of Polycystic Ovary Syndrome Using Random Forest with Bagging Technique

Amjed Al-Mousa, Badr Mansour, Hamsa Al-Dabbagh, Mohammad Radi

2023 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT)(2023)

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Abstract
The goal of this research is to aid doctors in the diagnosis of PCOS in female patients. Diagnosing the condition in question depends on many factors making it complex to diagnose. The model developed would help confirm a doctor's diagnosis to further its reliability. The model tested several classifiers, including Extreme Gradient Boosting (XGBoost), Linear Discriminant Analysis (LDA), and Adaptive Boosting (Ada-Boost). The highest accuracy was 94.4% using the Random Forest classifier with the Bagging method. This accuracy surpasses any previously achieved results using the same dataset, which were 91% and 92%. The results achieved were using a 10-Fold cross-validation.
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Key words
Machine Learning,Classification,Polycystic Ovary Syndrome,PCOS Diagnosis,Random Forest,Bagging,Kerala India Dataset
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