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Machine Learning–Based Prediction of Neurodegenerative Disease in Patients With Type 2 Diabetes by Derivation and Validation in 2 Independent Korean Cohorts: Model Development and Validation Study

Machine Learning–Based Prediction of Neurodegenerative Disease in Patients With Type 2 Diabetes by Derivation and Validation in 2 Independent Korean Cohorts: Model Development and Validation Study

Because the Ada Boost model yielded the highest AUROC score among the various decision tree models tested, we selected this model to identify the most important features for predicting ND. The importance of each feature was extracted using the feature importance attributes of the Ada Boost model. We selected the top 15 features with the largest impact on the model and plotted them on a bar graph to visualize their influence on the model predictions.

Hyunji Sang, Hojae Lee, Jaeyu Park, Sunyoung Kim, Ho Geol Woo, Ai Koyanagi, Lee Smith, Sihoon Lee, You-Cheol Hwang, Tae Sun Park, Hyunjung Lim, Dong Keon Yon, Sang Youl Rhee

J Med Internet Res 2024;26:e56922

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A Machine Learning Approach to Predict the Outcome of Urinary Calculi Treatment Using Shock Wave Lithotripsy: Model Development and Validation Study

A Machine Learning Approach to Predict the Outcome of Urinary Calculi Treatment Using Shock Wave Lithotripsy: Model Development and Validation Study

To predict the treatment outcome for SWL candidates, we used the Ada Boost algorithm based on the ensemble learning method, a machine learning technique that combines several base classifiers in various formats to produce a more robust and optimal classification model. Compared to other conventional machine learning algorithms, ensemble learning techniques are more stable, faster, simpler, and easier to program [15-19].

Reihaneh Moghisi, Christo El Morr, Kenneth T Pace, Mohammad Hajiha, Jimmy Huang

Interact J Med Res 2022;11(1):e33357

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Machine Learning–Based Prediction of Growth in Confirmed COVID-19 Infection Cases in 114 Countries Using Metrics of Nonpharmaceutical Interventions and Cultural Dimensions: Model Development and Validation

Machine Learning–Based Prediction of Growth in Confirmed COVID-19 Infection Cases in 114 Countries Using Metrics of Nonpharmaceutical Interventions and Cultural Dimensions: Model Development and Validation

We also include the median percent error [87], which is the percentage difference of the prediction f(x(i)) and the label y(i) for each instance {x(i), y(i)}, computed as: We observed that random forest regression had the lowest mean test error in the interpolation method (0.031) and adaptive boosting (Ada Boost) regression had the lowest mean test errors in the extrapolation and cross-validation methods (0.089 and 0.167, respectively) (see Table 5, Table 6, and Table 7).

Arnold YS Yeung, Francois Roewer-Despres, Laura Rosella, Frank Rudzicz

J Med Internet Res 2021;23(4):e26628

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