The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Enhancing the Healthcare by an Automated Detection Method for PCOS Using Supervised Machine Learning Algorithm
Abstract
Identifying Poly Cystic Ovary Syndrome (PCOS) can be challenging due to several factors like wide spectrum of symptoms, that varies according to individual, overlapping symptoms with other conditions like thyroid disorders, adrenal gland disorders, and ovarian tumors. Many women tend to overlook the common indications of PCOS and only seek medical attention when they encounter difficulties in conceiving. The integration of Machine Learning (ML) tools into clinical practice in collaboration with healthcare professionals have guaranteed proper interpretation and clinical decision-making. In this research, we explored the feasibility of developing a model that utilizes ML algorithms and techniques to automate the diagnosis of PCOS. To accomplish this, we utilized a dataset containing information from 543 subjects, encompassing 42 features such as metabolic, imaging, hormonal, and biochemical parameters. Initially, data pre-processing is performed, followed by the implementation of feature selection approach to reduce the number of features. Subsequently, various classification algorithms were trained and evaluated using Decision Tree (DT), XG Boost, Ada Boost, Random Forest (RF) and Logistic Regression (LR). After conducting a comprehensive analysis, we determined that the XG Boost outperformed the other algorithms in terms achieving high accuracy 92.45%.
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In the citing paper
“The majority of these studies originated from India (n = 5) [63,64,65,66,88], while three originated from the USA (n = 3) [67,87,89], and one from New Zealand [76].”
From Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review (2024) · cites this paper as [63]
In the citing paper
“Most of them were published in 2023 (n = 5) [63,64,65,76,87], while two were published in 2022 [88,89], and one each in 2021 [66] and 2020 [67].”
From Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review (2024) · cites this paper as [63]
In the citing paper
“Nine included studies mention the use of machine learning [63,64,65,66,67,76,87,88,89] (Supplementary File S3).”
From Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review (2024) · cites this paper as [63]
In the citing paper
“The latter used follicular fluid samples, while other studies using patient data reported an accuracy of 90.44% [65], 92.45% [63], and 98.41% [64].”
From Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review (2024) · cites this paper as [63]
Citations
Cites
PCOS Physiopathology and Vitamin D Deficiency: Biological Insights and Perspectives for Treatment
Morgante, G., Darino, I., Spanò, A., Luisi, S., Luddi, A., Piomboni, P., Governini, L., & De Leo, V. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11154509 · Cited by 95
Research works printed but not linked: 22
- 10.1002/9781394175130.ch8 · DOI
- 10.1016/j.coph.2022.102345 · DOI
- 10.1109/ICCES57224.2023.10192823 · DOI
- 10.1109/TEMSMET51618.2020.9557447 · DOI
- 10.1109/ICAISC58445.2023.10200380 · DOI
- Ali, 2019, DPCCG-EJA: Detection of key Pathways and Cervical Cancer related Genes using Enhanced Johnsons Algorithm, International Journal of Advanced Science and Technology, 28, 124
- 10.1109/ICAECIS58353.2023.10170077 · DOI
- 10.1007/s42979-022-01624-w · DOI
- 10.1109/TENCON.2019.8929674 · DOI
- Patil, 2023, Approaches for Network Analysis in Protein Interaction Network, 2, 47
- 10.1007/978-981-19-0825-5_20 · DOI
- Rethinavalli, 2016, A Hypothesis Analysis on the Proposed Methodology for Prediction of Polycystic Ovarian Syndrome, International Journal of Science, Engineering and Computer Technology 6, 11, 396
- 10.1109/RTEICT.2017.8256879 · DOI
- Pijush, 2021, An efficient SMOTE based machine learning classification for prediction & detection of PCOS
- 10.1109/TENSYMP50017.2020.9230932 · DOI
- 10.1007/s12652-021-03036-9 · DOI
- 10.3390/diagnostics13081506 · DOI · Paper page
- 10.1109/CCWC51732.2021.9375994 · DOI
- Ali, 2019, Consensus pattern selection from structured profile using multiobjective algorithm, Int J Adv Sci Technol., 28, 294
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Cited by
Sorted by citations
Polycystic Ovary Syndrome and the Internet of Things: A Scoping Review
Graca, S., Alloh, F., Lagojda, L., Dallaway, A., Kyrou, I., Randeva, H. S., & Kite, C. (2024) · Healthcare · doi:10.3390/healthcare12161671 · Cited by 14
“Five other studies that mention mobile apps were described in the overall context of machine learning [63–67] (Supplementary File S3).”
Background
Metadata
- Authors
- A, A., Ali, A., H V, R., A, M. S., & Nadaf, T. A.
- Affiliations
- School of CSE, REVA University,Bangalore,India,560064
- REVA University (IN)
- Wipro Arabia Ltd,Saudi Arabia
- ORCID
- Ali, Anooja
- Venue
- 2023 International Conference on Recent Advances in Information Technology for Sustainable Development (ICRAIS)
- Publisher
- IEEE
- Published
- 2023-11-06
- Pages
- 166-170
- OpenAlex
- W4390279547
- Cited by
- 13
- References
- 1
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 5
- FWCI
- 3.60
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Recent Advances in Information Technology for Sustainable Development (ICRAIS) (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Reproductive Medicine › Ovarian function and disorders
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- polycystic ovary syndrome, automated diagnosis, PCOS classification, XGBoost, feature selection, clinical decision support
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- CoChat. (2026). Citation check: Enhancing the Healthcare by an Automated Detection Method for PCOS Using Supervised Machine Learning Algorithm. https://app.cochat.ai/papers/10.1109/icrais59684.2023.10367055
- The paper
- A, A., Ali, A., H V, R., A, M. S., & Nadaf, T. A. (2023). Enhancing the Healthcare by an Automated Detection Method for PCOS Using Supervised Machine Learning Algorithm. 2023 International Conference on Recent Advances in Information Technology for Sustainable Development (ICRAIS). https://doi.org/10.1109/icrais59684.2023.10367055
