Basma Boukenze
ABSTRACT
Kidney failure is a silent chronic disease, without symptoms, and develops into an acute stage quickly. Unfortunately, the majority of patients discover this disease in its advanced stages where hemodialysis or transplant becomes a necessity. Predicting kidney failure will make controlling the progression of the disease possible and can even stabilize the state of patients. Data Analytics and data mining techniques, as well as machine learning proved a great success in information and knowledge discovery from medical big data to help decision making. Indeed, all the fields of the medical sector are paramount by the application of these technologies, given the useful knowledge extracted from collected data. Such knowledge will contribute to improve decision-making and save human lives. In this paper, we will present the big role of data mining technics in medical field in matters of predicting chronic diseases. Also we will show how these technics are very crucial to extract useful knowledge, and that by using linear regression model to predict glomerular filtration rate values (GFR) as the best indicator of kidney failure. The main purpose is to allow doctors to help patients, predict kidney failure and control this unwanted disease.
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