Proposed Model for Detection of PCOS Using Machine learning methods and Feature Selection

Authors

  • University of Thi-Qar, College of Education for Pure Sciences, Iraq
  • University of Thi-Qar, College of Education for Pure Sciences, Iraq

DOI:

https://doi.org/10.32792/jeps.v13i1.250

Keywords:

pcos, KNN, SVM, Machine Learning, Genetic algorithm

Abstract

PCOS (Polycystic Ovary Syndrome) is an endocrine disorder that affects women of reproductive age.
Once diagnosed, the condition cannot be cured, but treatment can help relieve its symptoms. Although
the exact cause of PCOS is unknown, certain risk factors do exist. Obesity, insulin resistance, blood
pressure, depression, and inflammation are all factors that contribute to this syndrome. While the
symptoms include hirsutism, oligo-ovulation, acne, heavy bleeding, and skin darkening. Using the causes
and symptoms, a model is created that accepts them as features and outputs the presence or absence of
this condition. Machine learning models used for classification include Linear SVM, Gaussian SVM,
Decision Tree, Nave Bayes, KNN1, KNN3, and KNN5. The goal of building multiple models is to find
the best one for a given dataset within the known scope of knowledge.

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Published

2023-04-10

Issue

Vol. 13 No. 1 (2023): Journal of Education for Pure Science- University of Thi-Qar

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