Abstract
(1) Background: Predictive modeling is becoming increasingly relevant in healthcare, aiding in clinical decision making and improving patient outcomes. However, many of the most potent predictive models, such as deep learning algorithms, are inherently opaque, and their decisions are challenging to interpret. This study addresses this challenge by employing Shapley Additive Explanations (SHAP) to facilitate model interpretability while maintaining prediction accuracy. (2) Methods: We utilized Gradient Boosting Machines (GBMs) to predict patient outcomes in an emergency department setting, with a focus on model transparency to ensure actionable insights. (3) Results: Our analysis identifies “Acuity”, “Hours”, and “Age” as critical predictive features. We provide a detailed exploration of their intricate interactions and effects on the model’s predictions. The SHAP summary plots highlight that “Acuity” has the highest impact on predictions, followed by “Hours” and “Age”. Dependence plots further reveal that higher acuity levels and longer hours are associated with poorer patient outcomes, while age shows a non-linear relationship with outcomes. Additionally, SHAP interaction values uncover that the interaction between “Acuity” and “Hours” significantly influences predictions. (4) Conclusions: We employed force plots for individual-level interpretation, aligning with the current shift toward personalized medicine. This research highlights the potential of combining machine learning’s predictive power with interpretability, providing a promising route concerning a data-driven, evidence-based healthcare future.
Share and Cite
MDPI and ACS Style
Feretzakis, G.; Sakagianni, A.; Anastasiou, A.; Kapogianni, I.; Βazakidou, Ε.; Koufopoulos, P.; Koumpouros, Y.; Koufopoulou, C.; Kaldis, V.; Verykios, V.S.
Integrating Shapley Values into Machine Learning Techniques for Enhanced Predictions of Hospital Admissions. Appl. Sci. 2024, 14, 5925.
https://doi.org/10.3390/app14135925
AMA Style
Feretzakis G, Sakagianni A, Anastasiou A, Kapogianni I, Βazakidou Ε, Koufopoulos P, Koumpouros Y, Koufopoulou C, Kaldis V, Verykios VS.
Integrating Shapley Values into Machine Learning Techniques for Enhanced Predictions of Hospital Admissions. Applied Sciences. 2024; 14(13):5925.
https://doi.org/10.3390/app14135925
Chicago/Turabian Style
Feretzakis, Georgios, Aikaterini Sakagianni, Athanasios Anastasiou, Ioanna Kapogianni, Εffrosyni Βazakidou, Petros Koufopoulos, Yiannis Koumpouros, Christina Koufopoulou, Vasileios Kaldis, and Vassilios S. Verykios.
2024. "Integrating Shapley Values into Machine Learning Techniques for Enhanced Predictions of Hospital Admissions" Applied Sciences 14, no. 13: 5925.
https://doi.org/10.3390/app14135925
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details
here.
Article Metrics
Article metric data becomes available approximately 24 hours after publication online.