PCOS PREDICTION USING MACHINE LEARNING TECHNIQUES: A COMPARATIVE ANALYSIS OF MODELS AND PRACTICAL APPLICATION
Abstract
Polycystic Ovary Syndrome (PCOS) is an endocrine disorder that affects women of reproductive age, and it is challenging to diagnose due to its clinical heterogeneity and symptom overlap with other conditions. The following study aims to investigate the use of Machine Learning (ML) techniques to enhance the diagnostic accuracy of PCOS, using a public dataset and comparing the classifiers Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF). Additionally, feature selection and data balancing techniques are applied to optimize the models. Finally, SOP ASSIST is proposed, an API that provides the patient's diagnostic result, considering the best trained model.
Author Biographies
Universidade Federal Rural do Semi-Árido (UFERSA).
Universidade Federal Rural do Semi-Árido - UFERSA.
Universidade Federal Rural do Semi-Árido - UFERSA.
Universidade Federal da Paraíba - UFPB.
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