K-Nearest Neighbor learning based diabetes mellitus prediction and analysis for eHealth services

Nowadays, eHealth service has become a booming area, which refers to computer-based health care and information delivery to improve health service locally, regionally and worldwide. An effective disease risk prediction model by analyzing electronic health data benefits not only to care a patient but also to provide services through the corresponding data-driven eHealth systems. In this paper, we particularly focus on predicting and analysing diabetes mellitus, an increasingly prevalent chronic disease that refers to a group of metabolic disorders characterized by a high blood sugar level over a prolonged period of time. K-Nearest Neighbor (KNN) is one of the most popular and simplest machine learning techniques to build such a disease risk prediction model utilizing relevant health data. In order to achieve our goal, we present an optimal K-Nearest Neighbor (Opt-KNN) learning based prediction model based on patient's habitual attributes in various dimensions. This approach determines the optimal number of neighbors with low error rate for providing better prediction outcome in the resultant model. The effectiveness of this machine learning eHealth model is examined by conducting experiments on the real-world diabetes mellitus data collected from medical hospitals.