This abstract presents a study on utilizing the Gradient Boosting algorithm for diabetes diagnosis. The objective is to develop a reliable and effective model that uses patient data, to detect the presence of diabetes. For training and testing, a dataset made up of clinical parameters like age, body mass index, blood pressure, and glucose levels are used. The Gradient Boosting algorithm is implemented and optimized to achieve optimal predictive performance. The model's accuracy, precision, recall, and F1 score are evaluated to assess its effectiveness. The results of this study indicate that the Gradient Boosting algorithm's effectiveness in correctly identifying diabetes and highlight its potential as a trustworthy tool for clinical diagnosis. In order to improve the model's performance and expand its application in real-world healthcare settings, future study can concentrate on adjusting its parameters and investigating new characteristics.

Gradient Boosting Algorithm Diabetes Diagnosis Patient Data Dataset Accuracy Precision Performance