Paper Contents
Abstract
Diabetes and Polycystic Ovary Syndrome (PCOS) are significant health issues that must be diagnosed as early as possible and classified correctly for proper treatment. In this study, three machine learning algorithms were used for the prediction of diabetes, and also PCOS. Data from clinical sources have been applied, including features such as glucose levels, insulin response, body mass index (BMI), and other relevant factors. The performances of the models were judged against the metrics such as accuracy, precision, recall, F1-score and AUC-ROC.
Copyright
Copyright © 2024 Sagar Parande. This is an open access article distributed under the Creative Commons Attribution License.
Paper ID:
IJPREMS41100044087
ISSN:
2321-9653
Publisher:
ijprems