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An Efficient Way of Detecting PCOS Using Machine Learning

EasyChair Preprint no. 7701

32 pagesDate: April 2, 2022


An endocrine disease that occurs in women of reproductive age is Polycystic Ovary Syndrome or PCOS. This is not necessary to reverse the disease once diagnosed, but medication may help alleviate the symptoms. The exact cause of PCOS is indeed unclear, but the probability of having PCOS is illustrated by some factors. Obesity, addiction to insulin, blood pressure, depression, infection are the causes that occur in this condition. Hirsutism, oligo-ovulation, acne, excessive bruising, and discoloration of the skin are the symptoms. A system is ready to accept them as characteristics and outputs of the inclusion or exclusion of this disorder using the causes and symptoms. K-Nearest Neighbor and Logistic Regression are the machine learning models used for supervised classification. The purpose behind the creation of multiple systems is to discover the perfect one of these in the known information spectrum for the given dataset.

Keyphrases: KNN, logistic regression, machine learning, supervised learning, Support Vector Machine

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Prashant Richhariya and Madhuri Nigam and Bharti Bhattad and Anita Soni and Pankaj Richhariya},
  title = {An Efficient Way of Detecting PCOS Using Machine Learning},
  howpublished = {EasyChair Preprint no. 7701},

  year = {EasyChair, 2022}}
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