Diabetes Mellitus Affected Patients Classification and Diagnosis through Machine Learning Techniques

Medical studies demonstrated that diabetes pathology is increasing in last decades and the trend do not tends to stop. In order to help and to accelerate the diagnosis of diabetes in this paper we propose a method able to classify patients affected by diabetes using a set of characteristic selected in according to World Health Organization criteria. Evaluating real-world data using state of the art machine learning algorithms, we obtain a precision value equal to 0.770 and a recall equal to 0.775 using the HoeffdingTree algorithm.