Kodipalli, A and Devi, S (2021) Prediction of PCOS and Mental Health Using Fuzzy Inference and SVM. In: Frontiers in Public Health, 9 .
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Abstract
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. Copyright © 2021 Kodipalli and Devi.
| Item Type: | Journal Article |
|---|---|
| Publication: | Frontiers in Public Health |
| Publisher: | Frontiers Media S.A. |
| Additional Information: | The copyright for this article belongs to the Author. |
| Department/Centre: | Division of Electrical Sciences > Computer Science & Automation |
| Date Deposited: | 05 Jan 2022 06:36 |
| Last Modified: | 05 Jan 2022 06:36 |
| URI: | http://eprints.iisc.ac.in/id/eprint/70897 |
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