ABSTRACT
In this Paper With the aid of AI techniques, this study aims to predict the early detection of chronic kidney disease, also known as chronic renal disease, in diabetic patients. It then suggests a decision tree to reach specific conclusions with desired accuracy by evaluating its performance in relation to its specification and sensitivity. Methods: The behaviour of learning algorithms based on a set of data mining indicators affects the models that are produced proportionately. Predicting the future is no longer a difficult task thanks to the promises of predictive analytics in big data and the use of machine learning algorithms, especially for the health sector, which has undergone significant evolution as a result of the development of new computer technologies that gave rise to numerous fields of study research. Many initiatives are made to deal with the explosion of medical data on the one hand, and to learn meaningful information from it, forecast diseases, and anticipate treatments on the other. To extract meaningful information and aid in decision-making, researchers used all the technological advancements, including big data analytics, predictive analytics, machine learning, and learning algorithms.