Improving Surgical Site Infection Prediction Using Machine Learning: Addressing Challenges of Highly Imbalanced Data.

Area of Science:

• Healthcare Informatics
• Medical Machine Learning
• Clinical Data Science

Background:

• Surgical site infections (SSIs) are a major cause of hospital readmissions and increased healthcare costs globally.
• Machine learning (ML) shows promise for predicting SSIs, but class imbalance remains a significant challenge.
• Accurate prediction of SSIs is crucial for mitigating patient harm and optimizing resource allocation.

Purpose of the Study:

• To evaluate and enhance ML model predictive capabilities for SSIs.
• To assess the impact of feature selection, resampling techniques, and hyperparameter optimization on SSI prediction accuracy.
• To identify optimal ML strategies for handling imbalanced datasets in SSI surveillance.

Main Methods:

• Utilized a dataset of 64,793 surgical patients from Saudi Arabian hospitals, with 1632 developing SSIs.
• Tested seven ML algorithms: Decision Tree, Gaussian Naive Bayes, Support Vector Machine, Logistic Regression, Random Forest, Stochastic Gradient Boosting, and K-Nearest Neighbors.
• Employed feature selection, resampling techniques (including SMOTE and IHT), and grid search cross-validation for hyperparameter optimization.

Main Results:

• Random Forest (RF) demonstrated the highest performance with a Matthews Correlation Coefficient (MCC) of 0.72.
• Synthetic Minority Oversampling Technique (SMOTE) generally improved model performance, except for Logistic Regression and Gaussian Naive Bayes.
• Instance Hardness Threshold (IHT) provided a computationally efficient undersampling alternative, albeit with potential performance trade-offs.

Conclusions:

• ML models are effective tools for assessing SSI risk, meriting further clinical investigation.
• Advanced ML techniques and robust validation, including MCC, provide reliable SSI prediction even with imbalanced data.
• Optimized ML approaches can significantly enhance the accuracy and reliability of SSI risk prediction, leading to better patient outcomes.