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Derivation of a Novel Diabetes Risk Score Using Semantic Discretization for Indian Population

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Ambient Communications and Computer Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 696))

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Abstract

The objective of this study is to derive a simple, yet effective type 2 Diabetes Risk Score Tool for Indian population using semantic discretization and machine learning techniques. The dataset used for training and validation is taken from Annual Health Survey, containing over 1.65 million people’s health-related information from 284 districts of India. This is the first study of its kind that truly represents the Indian population. A combination of feature selections techniques is used to find the minimal subset of attributes that optimally contribute in determining the class attribute. Continuous independent variables (various diabetes risk factors) are discretized using semantic discretization technique. The discretized dataset is then used in deriving Weighted Diabetes Risk Score for each risk factor. An optimal cutoff value for Total Weighted Diabetes Risk Score (TWDRS) is determined based on the evaluation parameters such as sensitivity, specificity, prediction accuracy, and proportion of population kept in high risk. The dataset used for this study contains 16,38,923 records. Records (7,42,605) that meet our criteria are selected for this study. Experimental results show that, at optimal cut point, TWDRS >=19, sensitivity is 72.55%, specificity is 61.99%, and proportion of population at high risk is 39.29%.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Uka Tarsadia University, Bardoli, Gujarat, India

    Omprakash Chandrakar

  2. Narmada College of Computer Application, Bharuch, Gujarat, India

    Jatinderkumar R. Saini

Authors
  1. Omprakash Chandrakar
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  2. Jatinderkumar R. Saini
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Correspondence to Omprakash Chandrakar .

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Editors and Affiliations

  1. University of Murcia, Murcia, Spain

    Gregorio Martinez Perez

  2. Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

    Shailesh Tiwari

  3. Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

    Munesh C. Trivedi

  4. Department of Computer Science and Engineering, Motilal Nehru National Institute of Technology, Allahabad, Uttar Pradesh, India

    Krishn K. Mishra

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Chandrakar, O., Saini, J.R. (2018). Derivation of a Novel Diabetes Risk Score Using Semantic Discretization for Indian Population. In: Perez, G., Tiwari, S., Trivedi, M., Mishra, K. (eds) Ambient Communications and Computer Systems. Advances in Intelligent Systems and Computing, vol 696. Springer, Singapore. https://doi.org/10.1007/978-981-10-7386-1_29

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  • DOI: https://doi.org/10.1007/978-981-10-7386-1_29

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  • Publisher Name: Springer, Singapore

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  • Online ISBN: 978-981-10-7386-1

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