Identification and epidemiological characterization of Type-2 diabetes sub-population using an unsupervised machine learning approach.

Journal: Nutrition & diabetes
Published Date: May 27, 2022

Abstract

BACKGROUND: Studies on Type-2 Diabetes Mellitus (T2DM) have revealed heterogeneous sub-populations in terms of underlying pathologies. However, the identification of sub-populations in epidemiological datasets remains unexplored. We here focus on the detection of T2DM clusters in epidemiological data, specifically analysing the National Family Health Survey-4 (NFHS-4) dataset from India containing a wide spectrum of features, including medical history, dietary and addiction habits, socio-economic and lifestyle patterns of 10,125 T2DM patients.

Authors

  • Saptarshi Bej
    Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock, Germany.
  • Jit Sarkar
    Division of Cell Biology and Physiology, CSIR-Indian Institute of Chemical Biology, Kolkata, India. jitnpur@gmail.com.
  • Saikat Biswas
    Advanced Technology Development Centre, Indian Institute of Technology, Kharagpur, India.
  • Pabitra Mitra
    Department of Computer Science & Engineering, Indian Institute of Technology, Kharagpur, India.
  • Partha Chakrabarti
    Division of Cell Biology and Physiology, CSIR-Indian Institute of Chemical Biology, Kolkata, India.
  • Olaf Wolkenhauer
    Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock, Germany.

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