Abstract:
In health services, artificial intelligence is used for early diagnosis functions to manage
huge quantities of medical studies with great accuracy and precision. PCOS is a hormonal
illness that affects a woman's menstrual cycle when she reaches reproductive age. Women
with PCOS have a menstrual cycle that lasts 21 days or longer. Women with PCOS might
have less periods (less than eight in a year) or stop having feminine cycles inside and out.
This can end in infertility including the appearance of cysts in the ovaries. Irregular
menstruation cycles, weight gain, skin darkening, thinning hair on the scalp, diabetes, and
high blood pressure are all symptoms of PCOS. It's better to have a diagnosis and treatment
as quickly as possible. Assortment of indications and the presence of an assortment of
gynecological disorders, PCOS is especially hard to analyze. The time and money spent on
the many clinical testing and ovarian scans has become a burden for PCOS sufferers. To
resolve this concern, this paper compares machine learning methods for the initial
prediction of PCOS using an ideal and basic but promising clinical and metabolic parameter
that serves as an early marker for the condition. To collect the data needed for this
comparative analysis, a patient questionnaire of 280 women was conducted during doctor
consultations and clinical examinations. Based on the significance of the 19 features from
medical and physiological test results, 12 prospective points are listed. In the Jupyter Python
IDE, PCOS is identified using various machine learning techniques such as logistic
regression, K-Nearest Neighbor (KNN), Gaussian Naive Bayes, Random Forest Classifier,
and Support Vector Machine (SVM). Random Forest Classifier (RFC) was discovered to
be the most appropriate and effective methodology for PCOS prediction, with an accuracy
of 100 percent.
