the Detection of PCOS Using Machine learning Algorithms and Feature Selection by K-Means clustering
DOI:
https://doi.org/10.32792/jeps.v13i1.251Keywords:
PCOS, Genetic algorithm, Machine learning, KNN, SVMAbstract
polycystic ovarian syndrome, which affects between 5 and 10 percent of adolescent women, is one of
the most prevalent endocrine system conditions (PCOS). Infertility and failure to ovulate are symptoms,
as are cardiovascular conditions, type 2 diabetes, etc. PCOS can be found by biochemical, clinical, and
ultrasonographic techniques. It is well recognized that early detection and intervention can lower the risk
of developing PCOS. Therefore, it is essential to understand which classification model and features
contribute significantly to the prediction of disease, which is the goal of this study. Despite the
employment of several tools, Naïve Bayes exhibits accuracy performances of 89.51% with 6 chosen
features.
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