K-Nearest Neighbor Learning based Diabetes Mellitus Prediction and Analysis for eHealth Services

Sarker, Iqbal H. and Faruque, Faisal and Alqahtani, Hamed and Kalim, Asra (2018) K-Nearest Neighbor Learning based Diabetes Mellitus Prediction and Analysis for eHealth Services. EAI Endorsed Transactions on Scalable Information Systems, 7 (26): e4. ISSN 2032-9407

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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. 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 KNearest 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.

Item Type: Article
Uncontrolled Keywords: health data analytics, diabetes mellitus, data science, machine learning, k-nearest neighbor, predictive analytics, classification, intelligent systems, eHealth, IoT services
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 08 Oct 2020 13:51
Last Modified: 08 Oct 2020 13:51
URI: https://eprints.eudl.eu/id/eprint/668

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