Detection of Chronic Kidney Disease Through Gradient Boosting Algorithm Combined with Feature Selection Techniques for Clinical Applications

CKD is a health crisis that's distribution worldwide. It involves a gradual decline in kidney functioning and is often severe if left untreated in its primary stages. Diagnosing diseases rapidly and correctly can improve health and more in patient health. Researchers have developed a way to detect a disease where people lose purpose of a kidney to prevent failing kidneys later Gradient boosting, a sophisticated method of compounding weak models, builds a strong classifier using complex data to show that the data is associated in unexpected ways. Feature selection comes into play where only the most relative variables are chosen, while the rest are discarded.The effectiveness of the method is tested on a public kidney disease data base; the technique is also well organised for data that does not complete. In the following, numerous methods for feature selection are tested. The gradient boosting model better at forecasting correct results compared to the baseline classifiers. This model is proven to be very reliable as it has an area under its ROC curve showing excellence in picking out patients having the disease.This new approach helps doctors know what's wrong early and gets them to the right treatment instead of the wrong way; it is very helpful. In clinical situations, the combination of gradient boosting and feature selection may prove successful in assessing the risk of CKD. The developers aim to improve the methods to determine recovery time for patients, but to make it better they need more data, and to really enhance the program to be more useful it needs to be tied up with real-time clinic programs.