Diabetes therapy prognosis through data stream mining methods and techniques

Abstract

Diabetes is one of the frequently occurring non-communicable diseases that lead causes of deaths among the worldwide. Maintain an appropriate blood glucose value for the patient needs a right amount of insulin dosage and the timing of its intake. But the medical interaction to the different lifestyle patients cause to the complexity of the therapy. In this article, a real-time classification therapy prognosis model is proposed to compute for regulating IDDM based on the daily prescription record and patients' individual blood glucose pattern by using data stream mining. A computer simulation is presented for evaluating the most appropriate data stream algorithms for this task.