Classifying Kidney Disease With AI Revolutionary Approach to Early Detection

Kidney disease is a major issue whose prevalence is a serious health concern that tends to develop without notice progressing to severe effects therefore proper diagnosis will ensure it is effectively controlled. This work provides a system designed to detect and classify kidney disease at the initial stage based on clinical and health-related data using artificial intelligence. The suggested method combines the models of machine learning and deep learning to detect the patterns related to kidney dysfunction. Various algorithms such as the Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Manual Neural Network architecture and Mobinet MobileNet architecture are tested with a stable K-Fold cross-validation model. Accuracy, precision, recall and F1-score are used to measure accuracy because the performance is rated reliably. Logistic Regression being one of the tested models has a better predictive ability of the data set given. The deployment of the system takes the form of a web application based on Django so that users could enter medical data and receive real-time predictions and analytical information. The provided work will promote the availability of screening of kidney disease using its data and encourages the medical treatment in time.