Prediction of Chronic Kidney Disease Progression Using CNN Based Machine Learning Models

The main points of the search would usually be summarized in the abstract of a study on the diagnosis of chronic kidney disease utilizing machine learning techniques. The project intends to use sophisticated ML algorithms to evaluate kidney CT scans in order to reliably and effectively classify different kidney disorders. The goal of the research is to improve the speed and accuracy of diagnosis by utilizing machine learning and image processing techniques, which will ultimately lead to more prompt and efficient medical interventions. The abstract can emphasize how artificial intelligence could transform kidney disease detection by analysing CT scans, leading to better patient outcomes and more effective healthcare delivery. The model was trained using machine learning classifiers, including Random Trees, K-Nearest Neighbours (KNN), Artificial Neural Networks (ANN), Naive Bayes and Convolutional Neural Networks. The validation and performance matrices for every classifier were computed.