PERFORMANCE OF DATA MINING TECHNIQUES TO PREDICT IN HEALTHCARE CASE STUDY : CHRONIC KIDNEY FAILURE DISEASE

With the promises of predictive analytics in big data, and the use of machine learning algorithms, predicting future is no longer a difficult task, especially for health sector, that has witnessed a great evolution following the development of new computer technologies that gave birth to multiple fields of research. Many efforts are done to cope with medical data explosion on one hand, and to obtain useful knowledge from it, predict diseases and anticipate the cure on the other hand. This prompted researchers to apply all the technical innovations like big data analytics, predictive analytics, machine learning and learning algorithms in order to extract useful knowledge and help in making decisions. In this paper, we will present an overview on the evolution of big data in healthcare system, and we will apply three learning algorithms on a set of medical data. The objective of this research work is to predict kidney disease by using multiple machine learning algorithms that are Support Vector Machine (SVM), Decision Tree (C4.5), and Bayesian Network (BN), and chose the most efficient one.

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