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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, USA

https://orcid.org/0000-0002-7504-8215 View further author information

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 China

https://orcid.org/0000-0003-3570-1918 View further author information

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]

https://orcid.org/0000-0003-1574-198X View further author information

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Figures & data

Table 1 Baseline Characteristics Of The 5,481 Participants

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Table 2 Variable Selection Results For LASSO, SCAD, MCP, Stepwise Logistic Regression, And ISIS

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Table 3 Performance Of Different Combination Methods Using The 11 Non-Invasive Score Systems In The Testing Population (n = 1,644)

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Table 4 Performance Of Different Combination Methods Using 9 Non-Invasive Score Systems Developed By Other Studies In The Testing Population (n = 1,644)

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Figure 1 Receiver operating characteristic curves for weighted voting, new score systems, and Western countries’ score systems.

Figure 2 Receiver operating characteristic curves for weighted voting, new score systems, and Eastern Asian score systems.

Figure 3 Receiver operating characteristic curves for majority voting and stacking using 9 existing score systems, new score systems, and Chinese score system.

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

Table 1 Baseline Characteristics Of The 5,481 Participants

Table 2 Variable Selection Results For LASSO, SCAD, MCP, Stepwise Logistic Regression, And ISIS

Table 3 Performance Of Different Combination Methods Using The 11 Non-Invasive Score Systems In The Testing Population (n = 1,644)

Table 4 Performance Of Different Combination Methods Using 9 Non-Invasive Score Systems Developed By Other Studies In The Testing Population (n = 1,644)

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

Figures & data

Table 1 Baseline Characteristics Of The 5,481 Participants

Table 2 Variable Selection Results For LASSO, SCAD, MCP, Stepwise Logistic Regression, And ISIS

Table 3 Performance Of Different Combination Methods Using The 11 Non-Invasive Score Systems In The Testing Population (n = 1,644)

Table 4 Performance Of Different Combination Methods Using 9 Non-Invasive Score Systems Developed By Other Studies In The Testing Population (n = 1,644)

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