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Risk Management and Healthcare Policy Volume 12, 2019 - Issue
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Original Research

Machine Learning For Tuning, Selection, And Ensemble Of Multiple Risk Scores For Predicting Type 2 Diabetes

Yujia Liu1 Department of Endocrinology and Metabolism, The First Hospital of Jilin University, Changchun, Jilin130021, People’s Republic of ChinaView further author information
,
Shangyuan Ye2 Department of Population Medicine, Harvard Pilgrim Health Care and Harvard Medical School, Boston, MA, USAORCID Iconhttps://orcid.org/0000-0002-7504-8215View further author information
ORCID Icon,
Xianchao Xiao1 Department of Endocrinology and Metabolism, The First Hospital of Jilin University, Changchun, Jilin130021, People’s Republic of ChinaView further author information
,
Chenglin Sun1 Department of Endocrinology and Metabolism, The First Hospital of Jilin University, Changchun, Jilin130021, People’s Republic of ChinaORCID Iconhttps://orcid.org/0000-0003-3570-1918View further author information
ORCID Icon,
Gang Wang1 Department of Endocrinology and Metabolism, The First Hospital of Jilin University, Changchun, Jilin130021, People’s Republic of ChinaView further author information
,
Guixia Wang1 Department of Endocrinology and Metabolism, The First Hospital of Jilin University, Changchun, Jilin130021, People’s Republic of ChinaCorrespondence[email protected]
View further author information
&
Bo Zhang3 Department of Neurology and ICCTR Biostatistics and Research Design Center, Boston Children’s Hospital and Harvard Medical School, Boston, MA, USACorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0003-1574-198XView further author information
ORCID Icon show all
Pages 189-198 | Published online: 05 Nov 2019

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Jie Song, Yuan Gao, Pengbin Yin, Yi Li, Yang Li, Jie Zhang, Qingqing Su, Xiaojie Fu & Hongying Pi. (2021) The Random Forest Model Has the Best Accuracy Among the Four Pressure Ulcer Prediction Models Using Machine Learning Algorithms. Risk Management and Healthcare Policy 14, pages 1175-1187.
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Alfredo De Giorgi, Emanuele Di Simone, Rosaria Cappadona, Benedetta Boari, Caterina Savriè, Pablo J López-Soto, María A Rodríguez-Borrego, Massimo Gallerani, Roberto Manfredini & Fabio Fabbian. (2020) Validation and Comparison of a Modified Elixhauser Index for Predicting In-Hospital Mortality in Italian Internal Medicine Wards. Risk Management and Healthcare Policy 13, pages 443-451.
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Articles from other publishers (8)

Daliya V. K. & T. K. Ramesh. (2023) Optimized stacking ensemble models for the prediction of diabetic progression. Multimedia Tools and Applications.
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Jorge A. Morgan-Benita, Carlos E. Galván-Tejada, Miguel Cruz, Jorge I. Galván-Tejada, Hamurabi Gamboa-Rosales, Jose G. Arceo-Olague, Huizilopoztli Luna-García & José M. Celaya-Padilla. (2022) Hard Voting Ensemble Approach for the Detection of Type 2 Diabetes in Mexican Population with Non-Glucose Related Features. Healthcare 10:8, pages 1362.
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Ashwini Tuppad & Shantala Devi Patil. (2022) Machine learning for diabetes clinical decision support: a review. Advances in Computational Intelligence 2:2.
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Monika Saraswat, A. K. Wadhwani & Sulochana Wadhwani. (2022) Predict the Chances of Heart Abnormality in Diabetic Patients Through Machine Learning. Journal on Artificial Intelligence 4:2, pages 61-76.
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Luis Fregoso-Aparicio, Julieta Noguez, Luis Montesinos & José A. García-García. (2021) Machine learning and deep learning predictive models for type 2 diabetes: a systematic review. Diabetology & Metabolic Syndrome 13:1.
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Nikos Fazakis, Otilia Kocsis, Elias Dritsas, Sotiris Alexiou, Nikos Fakotakis & Konstantinos Moustakas. (2021) Machine Learning Tools for Long-Term Type 2 Diabetes Risk Prediction. IEEE Access 9, pages 103737-103757.
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Sanjay Basu, Karl T. Johnson & Seth A. Berkowitz. (2020) Use of Machine Learning Approaches in Clinical Epidemiological Research of Diabetes. Current Diabetes Reports 20:12.
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Martina Vettoretti, Enrico Longato, Alessandro Zandonà, Yan Li, José Antonio Pagán, David Siscovick, Mercedes R Carnethon, Alain G Bertoni, Andrea Facchinetti & Barbara Di Camillo. (2020) Addressing practical issues of predictive models translation into everyday practice and public health management: a combined model to predict the risk of type 2 diabetes improves incidence prediction and reduces the prevalence of missing risk predictions. BMJ Open Diabetes Research & Care 8:1, pages e001223.
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