A Novel Approach for Polycystic Ovary Syndrome Prediction Using Machine Learning in Bioinformatics
Polycystic ovary syndrome (PCOS) is a critical disorder in women during their reproduction phase. The PCOS disorder is commonly caused by excess male hormone and androgen levels. The follicles are the collections of fluid developed by ovaries and may fail to release eggs regularly. The PCOS results...
| Main Authors: | , , , , |
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| Format: | Article |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9885199/ |
| _version_ | 1828414898836602880 |
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| author | Shazia Nasim Mubarak Saad Almutairi Kashif Munir Ali Raza Faizan Younas |
| author_facet | Shazia Nasim Mubarak Saad Almutairi Kashif Munir Ali Raza Faizan Younas |
| author_sort | Shazia Nasim |
| collection | DOAJ |
| description | Polycystic ovary syndrome (PCOS) is a critical disorder in women during their reproduction phase. The PCOS disorder is commonly caused by excess male hormone and androgen levels. The follicles are the collections of fluid developed by ovaries and may fail to release eggs regularly. The PCOS results in miscarriage, infertility issues, and complications during pregnancy. According to a recent report, PCOS is diagnosed in 31.3% of women from Asia. Studies show that 69% to 70% of women did not avail of a detecting cure for PCOS. A research study is needed to save women from critical complications by identifying PCOS early. The main aim of our research is to predict PCOS using advanced machine learning techniques. The dataset based on clinical and physical parameters of women is utilized for building study models. A novel feature selection approach is proposed based on the optimized chi-squared (CS-PCOS) mechanism. The ten hyper-parametrized machine learning models are applied in comparison. Using the novel CS-PCOS approach, the gaussian naive bayes (GNB) outperformed machine learning models and state-of-the-art studies. The GNB achieved 100% accuracy, precision, recall, and f1-scores with minimal time computations of 0.002 seconds. The k-fold cross-validation of GNB achieved a 100% accuracy score. The proposed GNB model achieved accurate results for critical PCOS prediction. Our study reveals that the dataset features prolactin (PRL), blood pressure systolic, blood pressure diastolic, thyroid stimulating hormone (TSH), relative risk (RR-breaths), and pregnancy are the prominent factors having high involvement in PCOS prediction. Our research study helps the medical community overcome the miscarriage rate and provide a cure to women through the early detection of PCOS. |
| first_indexed | 2024-12-10T13:34:57Z |
| format | Article |
| id | doaj.art-9de02288411a4b6da4c68eff1ae9a75e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-10T13:34:57Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9de02288411a4b6da4c68eff1ae9a75e2022-12-22T01:46:51ZengIEEEIEEE Access2169-35362022-01-0110976109762410.1109/ACCESS.2022.32055879885199A Novel Approach for Polycystic Ovary Syndrome Prediction Using Machine Learning in BioinformaticsShazia Nasim0Mubarak Saad Almutairi1https://orcid.org/0000-0001-6228-7455Kashif Munir2Ali Raza3https://orcid.org/0000-0001-5429-9835Faizan Younas4Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, PakistanCollege of Computer Science and Engineering, University of Hafr Al Batin, Hafr Alabtin, Saudi ArabiaFaculty of Computer Science and IT, Khawaja Fareed University of Engineering & IT, Rahim Yar Khan, PakistanDepartment of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, PakistanDepartment of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, PakistanPolycystic ovary syndrome (PCOS) is a critical disorder in women during their reproduction phase. The PCOS disorder is commonly caused by excess male hormone and androgen levels. The follicles are the collections of fluid developed by ovaries and may fail to release eggs regularly. The PCOS results in miscarriage, infertility issues, and complications during pregnancy. According to a recent report, PCOS is diagnosed in 31.3% of women from Asia. Studies show that 69% to 70% of women did not avail of a detecting cure for PCOS. A research study is needed to save women from critical complications by identifying PCOS early. The main aim of our research is to predict PCOS using advanced machine learning techniques. The dataset based on clinical and physical parameters of women is utilized for building study models. A novel feature selection approach is proposed based on the optimized chi-squared (CS-PCOS) mechanism. The ten hyper-parametrized machine learning models are applied in comparison. Using the novel CS-PCOS approach, the gaussian naive bayes (GNB) outperformed machine learning models and state-of-the-art studies. The GNB achieved 100% accuracy, precision, recall, and f1-scores with minimal time computations of 0.002 seconds. The k-fold cross-validation of GNB achieved a 100% accuracy score. The proposed GNB model achieved accurate results for critical PCOS prediction. Our study reveals that the dataset features prolactin (PRL), blood pressure systolic, blood pressure diastolic, thyroid stimulating hormone (TSH), relative risk (RR-breaths), and pregnancy are the prominent factors having high involvement in PCOS prediction. Our research study helps the medical community overcome the miscarriage rate and provide a cure to women through the early detection of PCOS.https://ieeexplore.ieee.org/document/9885199/Bioinformaticsdata analysisinfertilitymachine learningpregnancy complicationspolycystic ovary syndrome |
| spellingShingle | Shazia Nasim Mubarak Saad Almutairi Kashif Munir Ali Raza Faizan Younas A Novel Approach for Polycystic Ovary Syndrome Prediction Using Machine Learning in Bioinformatics IEEE Access Bioinformatics data analysis infertility machine learning pregnancy complications polycystic ovary syndrome |
| title | A Novel Approach for Polycystic Ovary Syndrome Prediction Using Machine Learning in Bioinformatics |
| title_full | A Novel Approach for Polycystic Ovary Syndrome Prediction Using Machine Learning in Bioinformatics |
| title_fullStr | A Novel Approach for Polycystic Ovary Syndrome Prediction Using Machine Learning in Bioinformatics |
| title_full_unstemmed | A Novel Approach for Polycystic Ovary Syndrome Prediction Using Machine Learning in Bioinformatics |
| title_short | A Novel Approach for Polycystic Ovary Syndrome Prediction Using Machine Learning in Bioinformatics |
| title_sort | novel approach for polycystic ovary syndrome prediction using machine learning in bioinformatics |
| topic | Bioinformatics data analysis infertility machine learning pregnancy complications polycystic ovary syndrome |
| url | https://ieeexplore.ieee.org/document/9885199/ |
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