Detection of PCOS using Machine Learning Algorithms with Grid Search CV Optimization

Detection of PCOS using Machine Learning Algorithms with Grid Search CV Optimization
  IJETT-book-cover           
  
© 2023 by IJETT Journal
Volume-71 Issue-7
Year of Publication : 2023
Author : K. Kavitha, Naresh Tangudu, Smita Rani Sahu, G V L Narayana, V. Anusha
DOI : 10.14445/22315381/IJETT-V71I7P219

How to Cite?

K. Kavitha, Naresh Tangudu, Smita Rani Sahu, G V L Narayana, V. Anusha, "Detection of PCOS using Machine Learning Algorithms with Grid Search CV Optimization," International Journal of Engineering Trends and Technology, vol. 71, no. 7, pp. 201-208, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I7P219

Abstract
Polycystic ovarian syndrome affects a lot of women who are of reproductive age (PCOS), a prevalent endocrine condition, develops. It has an impact on the female reproductive system, leading to polycystic ovaries, hyperandrogenism, and/or Ano/Oligo ovulation. Menstrual irregularities or high levels of androgen (male hormone) can occur in women with PCOS. The ovaries may create a great deal of small follicle clusters (cysts) and stop regularly producing eggs. Some signs of PCOS are period irregularities, an excess of androgen, polycystic ovaries, an abnormal BMI, imbalanced hormone levels, and decreased insulin sensitivity. In order to address this problem, a PCOS early detection app was developed using machine learning techniques. This study investigated the feasibility of creating an automated model to diagnose PCOS using machine learning techniques such as LightGBM Boost, Gradient Boost, and XGBoost, then using it with optimization methodology. For the best accuracy, grid search CV for hyperparameter tweaking. This conclusion was reached based on their statistical analysis of the data value on the earlier data set observations. The results are evaluated in terms of accuracy, recall, f1_scorings, and precision and are automated for real-life usage as web-based research.

Keywords
Hyperandrogenism, Ano/Oligo ovulation, Polycystic, Follicles, Statistical analysis, Grid search CV.

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