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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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-11-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2021.789569/full |
| _version_ | 1818722167453384704 |
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| author | Ashwini Kodipalli Ashwini Kodipalli Susheela Devi Susheela Devi |
| author_facet | Ashwini Kodipalli Ashwini Kodipalli Susheela Devi Susheela Devi |
| author_sort | Ashwini Kodipalli |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-17T20:50:19Z |
| format | Article |
| id | doaj.art-892a09efe8104ac4a21517f1ac6df0ec |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-12-17T20:50:19Z |
| publishDate | 2021-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-892a09efe8104ac4a21517f1ac6df0ec2022-12-21T21:33:03ZengFrontiers Media S.A.Frontiers in Public Health2296-25652021-11-01910.3389/fpubh.2021.789569789569Prediction of PCOS and Mental Health Using Fuzzy Inference and SVMAshwini Kodipalli0Ashwini Kodipalli1Susheela Devi2Susheela Devi3Department of Computer Science and Automation, Indian Institute of Science, Bengaluru, IndiaDepartment of Artificial Intelligence and Data Science, Global Academy of Technology, Bengaluru, IndiaDepartment of Computer Science and Automation, Indian Institute of Science, Bengaluru, IndiaDepartment of Artificial Intelligence and Data Science, Global Academy of Technology, Bengaluru, IndiaPolycystic 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.https://www.frontiersin.org/articles/10.3389/fpubh.2021.789569/fullsupport vector machinesfuzzy TOPSISfuzzy AHPpolycystic ovarian syndromemental health issuesmachine learning |
| spellingShingle | Ashwini Kodipalli Ashwini Kodipalli Susheela Devi Susheela Devi Prediction of PCOS and Mental Health Using Fuzzy Inference and SVM Frontiers in Public Health support vector machines fuzzy TOPSIS fuzzy AHP polycystic ovarian syndrome mental health issues machine learning |
| title | 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_short | Prediction of PCOS and Mental Health Using Fuzzy Inference and SVM |
| title_sort | prediction of pcos and mental health using fuzzy inference and svm |
| topic | support vector machines fuzzy TOPSIS fuzzy AHP polycystic ovarian syndrome mental health issues machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2021.789569/full |
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