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Identifying the Relative Importance of Factors Influencing Medication Compliance in General Patients Using Regularized Logistic Regression and LightGBM: Web-Based Survey Analysis

Identifying the Relative Importance of Factors Influencing Medication Compliance in General Patients Using Regularized Logistic Regression and LightGBM: Web-Based Survey Analysis

To address this issue, we use a recently developed model, Light GBM, which combines multiple decision trees and offers the advantages of high accuracy and low computational cost [42,43]. Using this model, the contribution of each variable to the response variable can be quantified as feature importance during model construction, facilitating an objective understanding of the importance of factors.

Haru Iino, Hayato Kizaki, Shungo Imai, Satoko Hori

JMIR Form Res 2024;8:e65882

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Predicting Abnormalities in Laboratory Values of Patients in the Intensive Care Unit Using Different Deep Learning Models: Comparative Study

Predicting Abnormalities in Laboratory Values of Patients in the Intensive Care Unit Using Different Deep Learning Models: Comparative Study

Third, we experimented with non-DL methods like Light GBM as well as 4 DL algorithms for time series classification. The DL-based method stacks models through mapping and processing functions between the models, using gradient descent or momentum methods to optimize fit.

Ahmad Ayad, Ahmed Hallawa, Arne Peine, Lukas Martin, Lejla Begic Fazlic, Guido Dartmann, Gernot Marx, Anke Schmeink

JMIR Med Inform 2022;10(8):e37658

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