Prediction of PCOS and Mental Health Using Fuzzy Inference and SVM

Polycystic ovarian syndrome (PCOS) is a hormonal disorder found in women of reproductive age. 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 automat...

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Autores principales:	Ashwini Kodipalli, Susheela Devi
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	support vector machines fuzzy TOPSIS fuzzy AHP polycystic ovarian syndrome mental health issues machine learning Public aspects of medicine RA1-1270
Acceso en línea:	https://doaj.org/article/892a09efe8104ac4a21517f1ac6df0ec
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id	oai:doaj.org-article:892a09efe8104ac4a21517f1ac6df0ec
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spelling	oai:doaj.org-article:892a09efe8104ac4a21517f1ac6df0ec2021-12-01T16:13:54ZPrediction of PCOS and Mental Health Using Fuzzy Inference and SVM2296-256510.3389/fpubh.2021.789569https://doaj.org/article/892a09efe8104ac4a21517f1ac6df0ec2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fpubh.2021.789569/fullhttps://doaj.org/toc/2296-2565Polycystic ovarian syndrome (PCOS) is a hormonal disorder found in women of reproductive age. 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 issues. In real-life applications, we often see that people are prompted to answer in linguistic terminologies to express their well-being in response to questions asked by the clinician. 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, the 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 the Fuzzy TOPSIS method and 94.01% using SVM was obtained. Along with the improvement in the performance and methodological contribution, the early detection and treatment of PCOS and mental health issues can together aid in taking preventive measures in advance. The psychological well-being of the women was also objectively evaluated and can be brought into the PCOS treatment protocol.Ashwini KodipalliAshwini KodipalliSusheela DeviSusheela DeviFrontiers Media S.A.articlesupport vector machinesfuzzy TOPSISfuzzy AHPpolycystic ovarian syndromemental health issuesmachine learningPublic aspects of medicineRA1-1270ENFrontiers in Public Health, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	support vector machines fuzzy TOPSIS fuzzy AHP polycystic ovarian syndrome mental health issues machine learning Public aspects of medicine RA1-1270
spellingShingle	support vector machines fuzzy TOPSIS fuzzy AHP polycystic ovarian syndrome mental health issues machine learning Public aspects of medicine RA1-1270 Ashwini Kodipalli Ashwini Kodipalli Susheela Devi Susheela Devi Prediction of PCOS and Mental Health Using Fuzzy Inference and SVM
description	Polycystic ovarian syndrome (PCOS) is a hormonal disorder found in women of reproductive age. 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 issues. In real-life applications, we often see that people are prompted to answer in linguistic terminologies to express their well-being in response to questions asked by the clinician. 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, the 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 the Fuzzy TOPSIS method and 94.01% using SVM was obtained. Along with the improvement in the performance and methodological contribution, the early detection and treatment of PCOS and mental health issues can together aid in taking preventive measures in advance. The psychological well-being of the women was also objectively evaluated and can be brought into the PCOS treatment protocol.
format	article
author	Ashwini Kodipalli Ashwini Kodipalli Susheela Devi Susheela Devi
author_facet	Ashwini Kodipalli Ashwini Kodipalli Susheela Devi Susheela Devi
author_sort	Ashwini Kodipalli
title	Prediction of PCOS and Mental Health Using Fuzzy Inference and SVM
title_short	Prediction of PCOS and Mental Health Using Fuzzy Inference and SVM
title_full	Prediction of PCOS and Mental Health Using Fuzzy Inference and SVM
title_fullStr	Prediction of PCOS and Mental Health Using Fuzzy Inference and SVM
title_full_unstemmed	Prediction of PCOS and Mental Health Using Fuzzy Inference and SVM
title_sort	prediction of pcos and mental health using fuzzy inference and svm
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/892a09efe8104ac4a21517f1ac6df0ec
work_keys_str_mv	AT ashwinikodipalli predictionofpcosandmentalhealthusingfuzzyinferenceandsvm AT ashwinikodipalli predictionofpcosandmentalhealthusingfuzzyinferenceandsvm AT susheeladevi predictionofpcosandmentalhealthusingfuzzyinferenceandsvm AT susheeladevi predictionofpcosandmentalhealthusingfuzzyinferenceandsvm
_version_	1718404827489239040

Prediction of PCOS and Mental Health Using Fuzzy Inference and SVM

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