Sensitivity analysis of physical and mental health factors affecting Polycystic ovary syndrome in women

Author:

Guha Srirupa12,Kodipalli Ashwini3

Affiliation:

1. National Institute of Technology Durgapur Durgapur India

2. Mercedes Benz Research and Development Bangalore India

3. Department of Artificial Intelligence and Data Science Global Academy of Technology Bangalore India

Abstract

AbstractPolycystic ovary syndrome (PCOS) is a common condition affecting women worldwide. In this paper, we analyse physical and mental health factors affecting PCOS in women. We collected data in the form of questionnaires from women belonging to diverse backgrounds and performed a sensitivity analysis to find which factors are significant in determining the presence or absence of PCOS in women and further predict the severity of the symptoms. We implemented four types of methods for determining significant physical and mental health factors affecting the presence and degree of PCOS in women. Our analysis showed that menstrual period cycle length, menstrual period duration, regularity of menstrual periods, menstrual period flow, hair growth, eating pattern, and sleep pattern are the most significant physical health factors and feeling of worthlessness, depression, feeling of hopelessness, not able to be calm, sad and can't cheer up, can't sit still and restlessness are the most significant mental health factors in determining the presence or absence of PCOS in an individual, as well as the degree of PCOS. Furthermore, physical factors are more significant than mental health factors in determining PCOS presence and degree in the cohort of female individuals considered for the analysis.

Publisher

Wiley

Subject

Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Control and Systems Engineering

Reference25 articles.

1. Detection of polycystic syndrome in ovary using machine learning algorithm;Alagarsamy M.;International Journal of Intelligent Systems and Applications in Engineering,2023

2. Prevalence and Characteristics of the Polycystic Ovary Syndrome in Overweight and Obese Women

3. Polycystic ovary syndrome: Current status and future perspective;Barthelmess E. K.;Frontiers in Bioscience (Elite Edition),2014

4. A review: Brief insight into Polycystic Ovarian syndrome

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