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– ABSTRACT
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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.
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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.
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In this paper, we particularly focus on predicting and analyzing 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.
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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.
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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.
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This approach determines the optimal number of neighbors with low error rate for
providing better prediction outcome in the resultant model.
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The effectiveness of this machine learning eHealth model is examined by conducting
experiments on the real-world diabetes mellitus data collected from medical hospitals.
