Detection of Polycystic Syndrome in Ovary Using Machine Learning Algorithm

Authors

  • Manjunathan Alagarsamy Department of Electronics and Engineering, K.Ramakrishnan College of Technology, Trichy - 621112, Tamil Nadu, India
  • Nithyadevi Shanmugam Department of Electronics and Communication Engineering, Dr.N.G.P. Institute of Technology, Coimbatore - 641048, Tamil Nadu, India
  • Dinesh Paramathi Mani Department of Electronics and Communication Engineering, Sona College of Technology, Salem, Tamil Nadu, India
  • Meenal Thayumanavan Department of Electronics and Communication Engineering, Kongunadu College of Engineering and Technology, Trichy - 621215, Tamil Nadu, India
  • K. Karpoora Sundari Department of Electronics and Communication Engineering, K.Ramakrishnan College of Technology, Trichy - 621112, Tamil Nadu, India
  • Kannadhasan Suriyan Department of Electronics and Communication Engineering, Study World College of Engineering, Coimbatore, Tamil Nadu, India

Keywords:

PCOS, SVM, KNN, Naïve Bayes, Ensemble, Evaluation Metrics

Abstract

PCOS is the reproductive metabolic condition in which the ovary produces the number of follicles that is unusually high. The number of follicles, size, and location of the ovary are observed using the data set of the ovary. Because of the varied sizes of follicles and the fact that it is strongly linked to veins and tissues, radiologists have traditionally had a tough time diagnosing PCOS. To predict the PCO syndrome fertility and infertility for the data collected the from KAGGLE repository, preprocessing techniques are being used to extract useful information for analysis. Heat Map is the preprocessing technique used for identifying correlated features. Then the extracted data are considered for training and testing to classify the occurrence and nonappearance of PCO syndrome. For data training and classification, Support Vector Machine, KNN, Naive Bayes, and Hybrid Algorithm are used. The proposed approach outperforms other current methods and has been proven to be effective.

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Flow Chart for PCO Analysis

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Published

16.01.2023

How to Cite

Alagarsamy, M. ., Shanmugam, N. ., Paramathi Mani, D. ., Thayumanavan, M. ., Sundari, K. K. ., & Suriyan, K. . (2023). Detection of Polycystic Syndrome in Ovary Using Machine Learning Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 11(1), 246–253. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2464

Issue

Vol. 11 No. 1 (2023)

Section

Research Article

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