Enhanced Diagnosis and Classification of Type 1 and Type 2 Diabetes Mellitus with Super Learner

Authors

  • Nisha A. Research Scholar Department of Computer Applications, B.S Abdur Rahman Crescent Institute of science and Technology, Chennai, Tamilnadu - 600048, India
  • Kavitha G. K Associate Professor Departrrent of lnformation Technology, B.S Abdur Rahman Crescent Institute of science and Technology, Chennai, Tamilnadu – 600048, India

Keywords:

Diabetes Mellitus, Machine Learning, Super Learner, Predictive Models

Abstract

Diabetes Mellitus (DM) plays a huge part in expanding the related medical issues overall by going about as a Comorbid condition. Besides, it is an ever-evolving disease without serious outer side effects prompting a deadly effect on the human body whenever left inconspicuous or untreated. This study aims to assess the risk of diabetes occurring as a comorbid condition by relating an individual's lifestyle and ethnic background. A detailed analysis of the lockdown's impact on people's rapid lifestyle changes brought on by the epidemic provides clear insight into how persons with diabetes mellitus become powerless. The chance of someone developing diabetes is predicted using a collection of machine learning computations. The Pima Indian dataset and the Vanderbilt biostatistics diabetes dataset, which show the effects of Type 1 diabetes mellitus, are used to create the ML model. The suggested super learner model produces the most remarkable classification accuracy of 97% for T1DM and T2DM when compared to an ensemble of algorithms in identifying and categorising people as being susceptible to DM because of their ethnic heritage and way of life.

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Published

24.03.2024

How to Cite

A., N. ., & G., K. . (2024). Enhanced Diagnosis and Classification of Type 1 and Type 2 Diabetes Mellitus with Super Learner . International Journal of Intelligent Systems and Applications in Engineering, 12(20s), 391–402. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5151

Issue

Vol. 12 No. 20s (2024)

Section

Research Article

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