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Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches

Author

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  • Ram D. Joshi

    (Department of Economics, Texas Tech University, Lubbock, TX 79409, USA
    These authors contributed equally to this work.)

  • Chandra K. Dhakal

    (Department of Agricultural and Applied Economics, University of Georgia, Athens, GA 30602, USA
    These authors contributed equally to this work.)

Abstract

Diabetes mellitus is one of the most common human diseases worldwide and may cause several health-related complications. It is responsible for considerable morbidity, mortality, and economic loss. A timely diagnosis and prediction of this disease could provide patients with an opportunity to take the appropriate preventive and treatment strategies. To improve the understanding of risk factors, we predict type 2 diabetes for Pima Indian women utilizing a logistic regression model and decision tree—a machine learning algorithm. Our analysis finds five main predictors of type 2 diabetes: glucose, pregnancy, body mass index (BMI), diabetes pedigree function, and age. We further explore a classification tree to complement and validate our analysis. The six-fold classification tree indicates glucose, BMI, and age are important factors, while the ten-node tree implies glucose, BMI, pregnancy, diabetes pedigree function, and age as the significant predictors. Our preferred specification yields a prediction accuracy of 78.26% and a cross-validation error rate of 21.74%. We argue that our model can be applied to make a reasonable prediction of type 2 diabetes, and could potentially be used to complement existing preventive measures to curb the incidence of diabetes and reduce associated costs.

Suggested Citation

  • Ram D. Joshi & Chandra K. Dhakal, 2021. "Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches," IJERPH, MDPI, vol. 18(14), pages 1-17, July.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:14:p:7346-:d:591341
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    References listed on IDEAS

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    1. Manal Alghamdi & Mouaz Al-Mallah & Steven Keteyian & Clinton Brawner & Jonathan Ehrman & Sherif Sakr, 2017. "Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-15, July.
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    Cited by:

    1. Yun Chen & Yiying Wang & Kelin Xu & Jie Zhou & Lisha Yu & Na Wang & Tao Liu & Chaowei Fu, 2021. "Adiposity and Long-Term Adiposity Change Are Associated with Incident Diabetes: A Prospective Cohort Study in Southwest China," IJERPH, MDPI, vol. 18(21), pages 1-12, October.
    2. Maya Maor & Moflah Ataika & Pesach Shvartzman & Maya Lavie Ajayi, 2021. "“I Had to Rediscover Our Healthy Food”: An Indigenous Perspective on Coping with Type 2 Diabetes Mellitus," IJERPH, MDPI, vol. 19(1), pages 1-16, December.
    3. Yi-Ching Lynn Ho & Vivian Shu Yi Lee & Moon-Ho Ringo Ho & Gladis Jing Lin & Julian Thumboo, 2021. "Towards a Parsimonious Pathway Model of Modifiable and Mediating Risk Factors Leading to Diabetes Risk," IJERPH, MDPI, vol. 18(20), pages 1-20, October.
    4. Malgorzata Grzelak & Paulina Owczarek & Ramona-Monica Stoica & Daniela Voicu & Radu Vilău, 2024. "Application of Logistic Regression to Analyze The Economic Efficiency of Vehicle Operation in Terms of the Financial Security of Enterprises," Logistics, MDPI, vol. 8(2), pages 1-14, May.
    5. Norma Latif Fitriyani & Muhammad Syafrudin & Siti Maghfirotul Ulyah & Ganjar Alfian & Syifa Latif Qolbiyani & Muhammad Anshari, 2022. "A Comprehensive Analysis of Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian Screening Scores for Diabetes Risk Assessment and Prediction," Mathematics, MDPI, vol. 10(21), pages 1-23, October.

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