Thesis (M.Sc.) - Cairo University - Faculty of Graduate Studies for Statistical Research- Department of Information System Technnology

Renal failure is a human surprise without seeing any complications except in the final stage, which is difficult to treat because the kidney function works despite the loss of about 85% to 90%, and this stage is called the end-stage. In order to protect ourselves from kidney failure, it is necessary to follow up on medical examinations and measure blood pressure and sugar periodically. In order for the patient to reach the End stage, he must pass through five stages of chronic renal failure until it quickly turns into acute kidney failure.Therefore, early detection of kidney disease contributes directly to preventing or stopping the spread of kidney failure as much as possible, which is spread epidemiologically not only in developing countries but also in most countries of the world. In this thesis, we used two data sources, applied different machine learning Algorithms, such as (Logistic Regression, Decision trees, Naïve Bayes, Random Forest, Support Vector Machines, K- Nearest Neighbors, Linear Discriminant Analysis and Ensemble (Voting Classifier)) ,The results of the experiments were very promising. An equation has also been proposed to measure the accuracy of the algorithms, and this equation differs from the accuracy of the model. Also, the application has been converted into a software that can be used in various work sites

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