sis 20(26): e4

Research Article

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

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  • @ARTICLE{10.4108/eai.13-7-2018.162737,
        author={Iqbal H. Sarker and Md. Faisal Faruque and Hamed Alqahtani and Asra Kalim},
        title={K-Nearest Neighbor Learning based Diabetes Mellitus Prediction and Analysis for eHealth Services},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={7},
        number={26},
        publisher={EAI},
        journal_a={SIS},
        year={2020},
        month={1},
        keywords={health data analytics, diabetes mellitus, data science, machine learning, k-nearest neighbor, predictive analytics, classification, intelligent systems, eHealth, IoT services},
        doi={10.4108/eai.13-7-2018.162737}
    }
    
  • Iqbal H. Sarker
    Md. Faisal Faruque
    Hamed Alqahtani
    Asra Kalim
    Year: 2020
    K-Nearest Neighbor Learning based Diabetes Mellitus Prediction and Analysis for eHealth Services
    SIS
    EAI
    DOI: 10.4108/eai.13-7-2018.162737
Iqbal H. Sarker1,2,*, Md. Faisal Faruque1, Hamed Alqahtani3,4, Asra Kalim5
  • 1: Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chittagong-4349, Bangladesh
  • 2: Swinburne University of Technology, VIC-3122, Australia
  • 3: King Khalid University, Saudi Arabia
  • 4: Macquarie University, NSW-2109, Australia
  • 5: Jazan University, Saudi Arabia
*Contact email: msarker@swin.edu.eu

Abstract

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.