Abstract
One of the major causes of infertility across the world is Polycystic Ovary Syndrome (PCOS) which has affected a large proportion of women. Its symptoms include hormone imbalance, irregular menstrual cycle, excess acne, weight gain and many more. If overlooked, the disease can lead to serious health problems like obstructive sleep apnea, type-2 diabetes, obesity, heart disease, mood disorders, etc. Thus, early detection of PCOS is important. This paper proposes a system for early prediction of PCOS using optimized machine learning classifiers. Extensive EDA was done on the dataset and out of 45 available features, 6 were selected using the Pearson statistical correlation coefficient. In addition to the EDA performed, oversampling was also done in order to handle the class imbalance problem by increasing the number of minority class samples. Optimized machine learning classifiers are classifiers with the best set of available hyperparameters that helps us achieve the highest prediction accuracy. Among the set of 11 optimized classifiers, Optimized KNN gave the best prediction accuracy of 0.995.
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Vora, K., Shah, A., Shah, N., Verma, P. (2023). Prediction of Polycystic Ovary Syndrome (PCOS) Using Optimized Machine Learning Classifiers. In: Mathur, G., Bundele, M., Tripathi, A., Paprzycki, M. (eds) Proceedings of 3rd International Conference on Artificial Intelligence: Advances and Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-7041-2_1
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DOI: https://doi.org/10.1007/978-981-19-7041-2_1
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