e.g. mhealth
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Skip search results from other journals and go to results- 8 Journal of Medical Internet Research
- 5 JMIR Medical Informatics
- 2 JMIR Diabetes
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We trained eight classification models including logistic regression, lasso regression, weighted k-nearest neighbor, decision tree, random forest, naive bayes, XGBoost, and support vector machine (SVM). In total, 70% of the dataset was allocated for model training and selection, while the remaining 30% was reserved for internal validation. We developed and validated the models stepwise through predictor preprocessing, model training, hyperparameter tuning, and 5-fold cross-validation.
JMIR Cancer 2025;11:e73069
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Extreme Gradient Boosting (XGBoost) is an ML algorithm that improves model performance by using a series of decision trees, where each tree corrects the mistakes of the previous one. This sequential approach helps make predictions more accurate. Light Gradient-Boosting Machine (Light GBM) is another efficient algorithm that works similarly to XGBoost but is designed to be faster and more scalable, especially when working with large datasets and many features.
JMIR Cardio 2025;9:e68066
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Extensive model evaluations, considering both AUROC and precision, revealed that the XGBoost model was the optimal model for predicting substance use among adolescents (Figures 2 and 3).
J Med Internet Res 2025;27:e62805
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We used Extreme Gradient Boosting (XGBoost), a robust ML framework known for its efficiency, flexibility, and portability [24]. It is an ensemble learning algorithm based on the gradient boosting framework, in which models are built sequentially to boost (ie, increase) the performance of the previous models by using the gradient descent algorithm to minimize errors [24].
JMIR Ment Health 2025;12:e66665
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Interpretable Machine Learning Model for Predicting Postpartum Depression: Retrospective Study
We used the following 3 machine learning algorithms to develop the PPD prediction model: extreme gradient boosting (XGBoost), random forest (RF), and gradient boosting machine (GBM). XGBoost is a powerful and efficient machine learning algorithm known for its exceptional performance in regression, classification, and ranking problems. It is an extension of the traditional gradient boosting method that combines multiple weak classifiers to create a strong classifier that minimizes the loss function [34].
JMIR Med Inform 2025;13:e58649
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This resulted in three datasets: (1) e Xtreme Gradient Boosting (XGBoost)-Mobile: mobile phone only, (2) XGBoost-Fitbit: Fitbit-only, and (3) XGBoost-Mobi Fit: combined mobile and Fitbit data. The rationale for choosing Machine Learning (ML) models is detailed in Multimedia Appendix 3 and model comparison with different classifiers can be found in Multimedia Appendix 4.
Marijuana use episodes and labeling principle.
JMIR AI 2025;4:e52270
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We did not use additional feature selection methods, relying instead on XGBoost to select relevant features in the construction of the final decision ensemble.
We used a simple piecewise linear interpolation technique to fill in missing values in Hb A1c records. Hb A1c imputation was done only between 2 known values—for example, if the 3-month and 9-month Hb A1c values for a patient were known, then the 6-month value was imputed as the average. We did not perform any other imputation.
JMIR Diabetes 2024;9:e53338
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Among the 4 models tested, XGBoost consistently yielded the highest AUROC scores across all data sets, leading to its selection as the primary model.
We performed hyperparameter tuning using Grid Search CV to optimize the performance of the XGBoost model, prioritizing the maximization of the AUROC score to determine the optimal hyperparameter combination.
J Med Internet Res 2024;26:e55913
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In this work, we used XGBoost for classification. XGBoost is a tree-based machine learning algorithm using gradient boosting [36] and has been widely used in medical diagnosis because of its high accuracy and performance, straightforward interpretation, and ability to handle missing values [43-47]. Logistic regression (LR), random forest (RF), and support vector classifier were also implemented for comparison.
J Med Internet Res 2023;25:e46520
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Early Triage of Critically Ill Adult Patients With Mushroom Poisoning: Machine Learning Approach
Four popular machine learning classification algorithms, including extreme gradient boosting (XGBoost) [32], random forest, support vector machine, and logistic regression, were applied in this study to build the classification models. We implemented machine learning algorithms using Python (version 3.9; Python Software Foundation) and several Python modules (panda, numpy, scipy, sklearn, xgboost, shap, and matplotlib).
JMIR Form Res 2023;7:e44666
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