Predicting Type 2 Diabetes Using Logistic Regression and Machine Learning Approaches
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References listed on IDEAS
- 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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- 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.
- 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.
- 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.
- 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.
- 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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Keywords
decision tree; diabetes risk factors; machine learning; prediction accuracy;All these keywords.
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