no code implementations • 16 Jan 2024 • Mulomba Mukendi Christian, Yun Seon Kim, Hyebong Choi, Jaeyoung Lee, Songhee You
This noise has the potential to diminish the performance of these algorithms, leading to inaccurate predictions.
no code implementations • 10 Jan 2024 • Christian Mulomba Mukendi, Hyebong Choi
Using the World Risk Index, we conduct a temporal analysis of global disaster risk dynamics from 2011 to 2021.
1 code implementation • 9 Jan 2024 • Mulomba Mukendi Christian, Hyebong Choi
The evaluation of several machine learning models demonstrates the effectiveness of the Random Forest algorithm in generating reliable predictions, particularly when applied to classification rather than regression, approach which enhances the model's generalizability by 42%, achieving a cross-validation score of 0. 38 for regression and 0. 89 for classification.
no code implementations • 9 Jan 2024 • Gabriel D. M. Manalu, Mulomba Mukendi Christian, Songhee You, Hyebong Choi
This study addresses this gap by proposing a novel approach that leverages patient prescription data as a modality to improve existing models for AKI prediction.