Abstract
Polycystic ovary syndrome (PCOS) is a hormone-related condition with the highest prevalence. The symptoms of PCOS include abnormal menstrual cycles, an increase in body and facial hair growth, weight gain, skin discoloration, diabetes, and infertility. Moreover, it was shown that those with PCOS have a wide range of metabolic issues. Up to 18% of women of reproductive age are affected with PCOS. The aim of this work is to use classification approaches to predict the presence of PCOS and evaluate how well different algorithms work. Moreover, it utilizes Synthetic Minority Oversampling Technique (SMOTE) and Correlation based feature selection method to develop a classifier model. The selected features are given to analyze seven different existing Machine Learning (ML) techniques, namely, Support Vector Machine (SVM), multi-layer SVM linear kernel with logistic regression, Random Forest (RF), Linear Regression (LR), Decision Tree (DT), K-Nearest Neighbor (KNN), and Artificial Neural Network (ANN) based on classification accuracy. The performance of the classifier is evaluated using PCOS dataset, which was provided from the Kaggle repository. According to evaluation results, SVM and ensemble-based RF classifiers have accuracy rates of 96.22% and 98.89%, respectively.
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Prasher, S., Nelson, L., Sharma, A. (2023). Evaluation of Machine Learning Techniques to Diagnose Polycystic Ovary Syndrome Using Kaggle Dataset. In: Rathore, V.S., Piuri, V., Babo, R., Ferreira, M.C. (eds) Emerging Trends in Expert Applications and Security. ICETEAS 2023. Lecture Notes in Networks and Systems, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-99-1946-8_25
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DOI: https://doi.org/10.1007/978-981-99-1946-8_25
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