Improving machine learning algorithm for risk of early pressure injury prediction in admission patients using...

Pressure injuries (PIs) pose a significant concern in hospital care, necessitating early and accurate prediction to mitigate adverse outcomes.

The proposed approach receives multiple patients records, selects key features of discrete numerical based on their relevance to PIs, and trains a random forest (RF) machine learning (ML) algorithm to build a predictive model. Pairs of significant categorical features with high contributions to the prediction results are grouped, and the PI risk probability for each group is calculated. High-risk group probabilities are then added as new features to the original feature subset, generating a new feature subset to replace the original one, which is then used to retrain the RF model.

The proposed method achieved an accuracy of 83.44%, sensitivity of 84.59%, specificity of 83.42%, and an area under the curve of 0.84.

The ML-based approach, coupled with feature aggregation, enhances predictive performance, aiding clinical teams in understanding crucial features and the model's decision-making process.