AUTHOR=Kodipalli Ashwini , Devi Susheela 

TITLE=Prediction of PCOS and Mental Health Using Fuzzy Inference and SVM

JOURNAL=Frontiers in Public Health

VOLUME=Volume 9 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2021.789569

DOI=10.3389/fpubh.2021.789569

ISSN=2296-2565

ABSTRACT=Poly Cystic Ovarian Syndrome (PCOS) is a hormonal disorder found in women of reproductive age. It includes failure to ovulate, infertility along with mental health complications. There are different methods used for the detection of PCOS, but these methods limitedly support the integration of PCOS and mental health issues. To address these issues, in this paper we present an automated early detection and prediction model which can accurately estimate the likelihood of having PCOS and associated mental health. In real life applications we often see that people are prompted to answer in linguistic terminologies to express their wellbeing for the questions asked by the clinician. In order to model the inherent linguistic nature of the mapping between symptoms and diagnosis of PCOS a fuzzy approach is used. Therefore, in the present study, Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is evaluated for its performance. Using the local yet specific dataset collected on a spectrum of women, the Fuzzy TOPSIS is compared with the widely used Support Vector Machines (SVM) algorithm. Both the methods are evaluated on the same dataset. An accuracy of 98.20% using Fuzzy TOPSIS method and 94.01% using SVM is obtained. Along with improvement in the performance, methodological contribution, the early detection and treatment of PCOS and mental health issues can together aid in taking preventive measures in advance. The psychological wellbeing of the women is also objectively evaluated and can be brought into the PCOS treatment protocol.