Abstract
In today’s world, women are struggling from variety of illnesses. The most common disease is polycystic ovary syndrome (PCOD); it is a common condition which has a major impact on women’s reproductive lives. The common symptoms of this condition are irregular menses, oily stools, anxiety disorders, acne and hypertension. In this paper, the significance of each attributes is highlighted, and the various data mining methods had to be combined in order to accurately predict this disease. Here, the algorithms considered are SVM, KNN, K-means, and linear regression. Compared to all, SVM shows the highest accuracy of 91%, whereas the accuracy of others algorithms accuracy is KNN 75%, K-means 72%, and linear regression 85%.
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Sinthia, G., Poovizhi, T., Khilar, R. (2022). Analysis on Polycystic Ovarian Syndrome and Comparative Study of Different Machine Learning Algorithms. In: Mohanty, M.N., Das, S. (eds) Advances in Intelligent Computing and Communication. Lecture Notes in Networks and Systems, vol 430. Springer, Singapore. https://doi.org/10.1007/978-981-19-0825-5_20
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DOI: https://doi.org/10.1007/978-981-19-0825-5_20
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