Proposed Model for Detection of PCOS Using Machine learning methods and Feature Selection

Authors

  • University of Thi-Qar, College of Education for Pure Sciences, Iraq
  • University of Thi-Qar, College of Education for Pure Sciences, Iraq

DOI:

https://doi.org/10.32792/jeps.v13i1.250

Keywords:

pcos, KNN, SVM, Machine Learning, Genetic algorithm

Abstract

PCOS (Polycystic Ovary Syndrome) is an endocrine disorder that affects women of reproductive age.
Once diagnosed, the condition cannot be cured, but treatment can help relieve its symptoms. Although
the exact cause of PCOS is unknown, certain risk factors do exist. Obesity, insulin resistance, blood
pressure, depression, and inflammation are all factors that contribute to this syndrome. While the
symptoms include hirsutism, oligo-ovulation, acne, heavy bleeding, and skin darkening. Using the causes
and symptoms, a model is created that accepts them as features and outputs the presence or absence of
this condition. Machine learning models used for classification include Linear SVM, Gaussian SVM,
Decision Tree, Nave Bayes, KNN1, KNN3, and KNN5. The goal of building multiple models is to find
the best one for a given dataset within the known scope of knowledge.

References

X. Zhang et al., “Raman spectroscopy of follicular fluid and plasma with machine-learning

algorithms for polycystic ovary syndrome screening,” Mol. Cell. Endocrinol., vol. 523, no.

December 2020, p. 111139, 2021, doi: 10.1016/j.mce.2020.111139.

K. Maheswari, T. Baranidharan, S. Karthik, and T. Sumathi, “Modelling of F3I based feature

selection approach for PCOS classification and prediction,” J. Ambient Intell. Humaniz. Comput.,

vol. 12, no. 1, pp. 1349–1362, 2021, doi: 10.1007/s12652-020-02199-1.

S. Bhosale, L. Joshi, and A. Shivsharan, “PCOS ( POLYCYSTIC OVARIAN SYNDROME )

DETECTION USING,” no. 01, pp. 195–200, 2022.

J. Madhumitha, M. Kalaiyarasi, and S. S. Ram, “Automated polycystic ovarian syndrome

identification with follicle recognition,” 2021 3rd Int. Conf. Signal Process. Commun. ICPSC

, no. May, pp. 98–102, 2021, doi: 10.1109/ICSPC51351.2021.9451720.

S. Bharati, P. Podder, and M. R. Hossain Mondal, “Diagnosis of Polycystic Ovary Syndrome

Using Machine Learning Algorithms,” 2020 IEEE Reg. 10 Symp. TENSYMP 2020, no. June, pp.

–1489, 2020, doi: 10.1109/TENSYMP50017.2020.9230932.

P. Soni and S. Vashisht, “Exploration on Polycystic Ovarian Syndrome and Data Mining

Techniques,” Proc. 3rd Int. Conf. Commun. Electron. Syst. ICCES 2018, no. Icces, pp. 816–820,

, doi: 10.1109/CESYS.2018.8724087.

M. S. Khan Inan, R. E. Ulfath, F. I. Alam, F. K. Bappee, and R. Hasan, “Improved Sampling and

Feature Selection to Support Extreme Gradient Boosting for PCOS Diagnosis,” 2021 IEEE 11th

Annu. Comput. Commun. Work. Conf. CCWC 2021, pp. 1046–1050, 2021, doi:

1109/CCWC51732.2021.9375994.

U. N. Wisesty and J. Nasri, “Modified backpropagation algorithm for polycystic ovary syndrome

detection based on ultrasound images,” in International Conference on Soft Computing and Data

Mining, 2016, pp. 141–151.

R. M. Dewi, Adiwijaya, U. N. Wisesty, and Jondri, “Classification of polycystic ovary based on

ultrasound images using competitive neural network,” J. Phys. Conf. Ser., vol. 971, no. 1, 2018,

doi: 10.1088/1742-6596/971/1/012005.

N. N. Xie, F. F. Wang, J. Zhou, C. Liu, and F. Qu, “Establishment and Analysis of a CombinedDiagnostic Model of Polycystic Ovary Syndrome with Random Forest and Artificial Neural

Network,” Biomed Res. Int., vol. 2020, 2020, doi: 10.1155/2020/2613091.

V. Fruh, J. J. Cheng, A. Aschengrau, S. Mahalingaiah, and K. J. Lane, “Fine particulate matter and

polycystic ovarian morphology,” Environ. Heal., vol. 21, no. 1, pp. 1–8, 2022, doi:

1186/s12940-022-00835-1.

C. Neto, M. Silva, M. Fernandes, D. Ferreira, and J. Machado, “Prediction models for Polycystic

Ovary Syndrome using data mining,” in International Conference on Advances in Digital Science,

Wang, J., Jain, S., Chen, D., Song, W., Hu, C.T., & Su, Y.H., Development and Evaluation of

Novel Statistical Methods in Urine Biomarker-Based Hepatocellular Carcinoma Screening. Sci Rep 8, 1

(2018) 3799.

www.kaggle.com/prasoonkottarathil/polycystic-ovary-syndrome-pcos. last accessed on 5 May

Azeez, R. A., Miften, F. S., & Hayawi, M. J. (2020). Epileptic EEG Signals Classification Based on

Determinant of Matrix as a Feature. 4. Journal of Global Scientific Research (ISSN: 2523-9376), 4, 461-465.

Al-Salman, W., Li, Y., Wen, P., Miften, F. S., Oudah, A. Y., & Al Ghayab, H. R. (2022). Extracting epileptic

features in EEGs using a dual-tree complex wavelet transform coupled with a classification algorithm. Brain

Research, 1779, 147777.

Miften, F. S. (2017). Using Quantum Particle Swarm Optimization to Enhance K-Means Clustering. Journal

of Education for Pure Science, 7(4).

Miften, F. S., Taher, H. B., & Ajeel, K. (2017). Fractal Image Compression using Quantum PSO. Journal of

Education for Pure Science, 7(2).

Hadi, I., & Miften, F. S. (2017). A Graph Clustering Algorithm Based on Adaptive Neighbors

Connectivity. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-11), 19-22.

Miften, F. S. (2014). Upgrade PIFS to Patterns Recognition

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Published

2023-04-10