Endometriosis is a recurrence disease that often stripes the uterus of women and creates an impact on women of fertility age. The stages of endometriosis have been predicted based on the intrusion of lesion, location affected and colour of endometriosis. Medical experts recognize the endometriosis through certain symptoms that include chronic pelvic pain, adnexal mass, size/colour of the lesion, and blockages in the tube. Machine learning is a cutting-edge technology in the field of disease recognition. There are various machine learning techniques that assist medical experts in recognizing the type and severity of endometriosis. The proposed approach invokes the five most popular supervised learning algorithms that perform well for medical-based data. The supervised algorithms include logistic regression, random forest, decision tree, K-nearest neighbour, and extreme gradient boosting used for execution. The dataset was spilt as 70% for training and 30% for validation. All five algorithms were executed with a test accuracy of 0.79 for logistic regression, 0.79 for the decision tree, 0.91 for the random forest, 0.92 for K-nearest neighbour, and 0.9 for gradient boosting algorithms. Also, the area under curve obtained for logistic regression was 0.82, decision tree was 0.82, random forest was 0.99, gradient boosting was 0.97, and K-nearest neighbour was 0.97. The random forest algorithm outperformed well in terms of area under curve of 0.99 for the given dataset in predicting the type of endometriosis.

endometriosis, random forest, decision tree, extreme gradient boosting, logistic regression.

Received: January 3, 2023; Accepted: March 17, 2023; Published: May 15, 2023

How to cite this article: S. Visalaxi, T. Sudalaimuthu and K. Hemapriya, Automated evaluation of supervised learning algorithm for endometriosis prediction, JP Journal of Biostatistics 23(2) (2023), 149-172. http://dx.doi.org/10.17654/0973514323009

This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License

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