Exploring machine learning approaches for early diabetes risk prediction: A comprehensive examination of health indicators and models

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Published: Jul 23, 2024
Keywords:
Health indicators, diabetes, classification, decision tree, random forest

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Nihar Ranjan Panda
Department of Medical Research, IMS & SUM Hospital, SOA Deemed to be university, Bhubaneswar, Odisha, India
Jatindra Nath Mohanty
School of Basic and Applied Science, Centurian University of Technology & Management, Bhubaneswar, Odisha, India
Ruchi Bhuyan
Department of Medical Research, IMS & SUM Hospital, SOA Deemed to be university, Bhubaneswar, Odisha, India
Prasanta Kumar Raut
Trident academy of technology, Bhubaneswar, Odisha, India
Manulata
All India Institute of Medical Science, Bibinagar, India

Abstract

Background: The increased prevalence of morbidity and mortality associated with Type 2 diabetes is due to changing lifestyles, demanding improved disease management measures. To tackle this, scientists are increasingly looking to technological advances, notably machine learning, for illness prevention and management, particularly in non-communicable diseases. The emphasis is on establishing an early detection system to identify Type 2 diabetes risk factors, enabling prompt treatments and preventative steps to reduce the disease’s rising prevalence.


Materials and methods: The research aimed to assess the association of diabetes class with health indicators. Five machine learning models were employed with cross-validation techniques to predict early diabetes risk. The performance matrices of the models were evaluated and compared with the existing work.


Results: In multivariate analysis, we found polyuria (β=3.492; Aor=32.872; 95% CI=11.09,97.35; p<0.001), polydipsia (β=-4.100; Aor=60.378; 95%CI=18.28,199.37; p<0.001), polyphagia (β=1.181; Aor=3.25; 95%CI=1.23,8.57; p=0.017), genital thrush (β=1.08; Aor=2.96; 95%CI=1.26,7.53; p=0.023), irritability (β=2.28; Aor=9.82; 95%CI=3.41,28.26; p<0.001), and partial paresis (β=1.2406; Aor=3.45; 95% CI=1.35,8.79; p=0.009) are the potential health risk indicators for positive diabetes class.


Conclusion: Using an interpretable feature learning approach for early diabetes prediction improves the use of global health data. This method forecasts hazards correctly and gives insights into influential aspects. As a result, a more proactive healthcare strategy is implemented, allowing for more prompt treatments and encouraging a more hopeful future by improving patient outcomes and lowering the total burden of diabetes on individuals and healthcare systems.

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How to Cite
Panda, N. R. ., Mohanty, J. N. ., Bhuyan, R., Raut, P. K. ., & Manulata. (2024). Exploring machine learning approaches for early diabetes risk prediction: A comprehensive examination of health indicators and models. Journal of Associated Medical Sciences, 57(3), 155–165. Retrieved from https://he01.tci-thaijo.org/index.php/bulletinAMS/article/view/271446
Issue
Vol. 57 No. 3 (2024): September-December
Section
Research Articles
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