Detection of PCOS Based on Genetic Algorithm Coupled with SVM
PCOS (polycystic ovary syndrome) is a hormonal illness that affects women's of life from a young age.
Human diagnosis of PCOS is a difficult undertaking for professionals; yet, rapid and accurate detection of
PCOS could save the lives of millions of women throughout the world. Current research employs highdimensional
features, resulting in low estimation accuracy and a long execution time. However, in this
research, we create a novel intelligence system based on a Genetic Algorithm linked with an SVM (AGSVM)
that uses fewer features to categorize PCOS. The dataset is preprocessed before a Genetic
Algorithm is used to select the most powerful features for PCOS classification. As a consequence, seven
features were selected from the feature set to represent PCOS data. The SVM is fed the selected
characteristics set in order to categorize them into healthy and non-healthy segments. Our results showed
that the suggested model (AG-SVM) achieved a 90% accuracy rate.
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