Using Machine Learning Technology to Distinguish Between Kidney Diseases

One of the most recent advancements in the diagnosis of diseases, including kidney illness, is medical imaging. The potential applications of artificial intelligence (machine learning) models to a variety of diseases in general, particularly kidney disease detection, data interpretation. In order to improve the images, which should be the same size and dimensions to show some image features, and to turn them into a high-quality medical image for use in the process of detecting and classifying kidney diseases, the image pre-processing procedure was then carried out to transform the original medical image data into medical image data free from some undesired distortions (noise). (MSD, PSDR, AD, MD, SSIM, SR-SIM, FSIM, RMSE, LMSE and CQ) were among the twenty statistical image metrics that were used to assess image quality. to assess how well the three classification algorithms RF, KNN, and SVM perform. Precision, accuracy, recall, and the F1 measure are among the performance indicators used to assess the efficacy of the categorization process. Achieving high accuracy results shows that the system works well and produces extremely precise outcomes.