Using Naïve Bayes to Predict In-Hospital Mortality in Elderly Hip Fracture Patients
Author Information
Author(s): Jo-Wai Douglas Wang
Primary Institution: The Canberra Hospital, ACT Health, Canberra, Australia
Hypothesis
Can Naïve Bayes effectively predict in-hospital mortality for elderly patients with hip fractures using only sociodemographic and comorbidity data?
Conclusion
Naïve Bayes is a simple and interpretable model that performs comparably to more complex machine learning algorithms in predicting in-hospital mortality for elderly hip fracture patients.
Supporting Evidence
- Naïve Bayes performed comparably to random forests and logistic regression in predicting mortality.
- Chronic kidney disease and cardiovascular comorbidities were identified as significant predictors of mortality.
- The model requires only easily obtainable data, making it practical for early risk assessment.
Takeaway
This study shows that a simple computer program can help doctors figure out which older patients with hip fractures are more likely to get very sick in the hospital.
Methodology
Seven machine learning models were developed and compared using sociodemographic and comorbidity data to predict in-hospital mortality, evaluated through cross-validation and SHAP analysis.
Potential Biases
Potential under-reporting of smoking and drinking habits may introduce bias.
Limitations
The study is based on a single-center retrospective cohort, which may limit the generalizability of the findings.
Participant Demographics
Median age of participants was 84 years, with 75.3% female and 5.2% experiencing in-hospital mortality.
Statistical Information
Statistical Significance
p>0.05
Digital Object Identifier (DOI)
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