International Research Journal of Education and Technology

This project aims to predict chronic kidney disease and assess their reliability through the analysis of machine learning. By collecting data resources from various reports and also from existing datasets. We process and analyze the collected data to extract valuable information. Machine learning models were developed for Testing the collected data to forecast the probability of finding kidney diseases. The results of the project will benefit the patients who are facing kidney disease and help them to save their lives. A significant public health issue that affects millions of individuals globally is chronic kidney disease (CKD). To stop CKD from progressing to end-stage renal disease (ESRD), which necessitates dialysis or transplantation, early identification and management are crucial. Machine learning techniques may be used to discover the patterns connected to CKD by learning from big datasets of patient data. Machine learning can be used to predict CKD with great accuracy, according to a number of studies. First of all, it is a cheap and non-invasive technique to check for CKD. In order to implement early therapies, it can be utilized to identify individuals who are very susceptible to developing CKD. Thirdly, it may be used to track the development of CKD and forecast when patients could require dialysis or a transplant. One problem is that depending on the dataset used to train the algorithm, the prediction accuracy might change. Machine learning is a potential method for the early diagnosis and prediction of CKD despite these difficulties. The algorithms will get more precise and user-friendly as they advance. Better early CKD identification and management will result in better patient outcomes as a result of this.

 Keywords :

Machine learning, Chronic kidney disease, prediction, early detection, management,Algorithms .