Constructing a prediction model for delayed wound healing after gastric cancer radical surgery based on three machine learning algorithms

Abstract

BACKGROUND

Delayed wound healing is a common clinical complication following gastric cancer radical surgery, adversely affecting patient prognosis. With advances in artificial intelligence, machine learning offers a promising approach for developing predictive models that can identify high-risk patients and support early clinical intervention.

AIM

To construct machine learning-based risk prediction models for delayed wound healing after gastric cancer surgery to support clinical decision-making.

METHODS

We reviewed a total of 514 patients who underwent gastric cancer radical surgery under general anesthesia from January 1, 2014 to December 30, 2023. Seventy percent of the dataset was selected as the training set and 30% as the validation set. Decision trees, support vector machines, and logistic regression were used to construct a risk prediction model. The performance of the model was evaluated using accuracy, recall, precision, F1 index, and area under the receiver operating characteristic curve and decision curve.

RESULTS

This study included five variables: Sex, elderly, duration of abdominal drainage, preoperative white blood cell (WBC) count, and absolute value of neutrophils. These variables were selected based on their clinical relevance and statistical significance in predicting delayed wound healing. The results showed that the decision tree model outperformed the logistic regression and support vector machine models in both the training and validation sets. Specifically, the decision tree model achieved higher accuracy, F1 index, recall, and area under the curve (AUC) values. The support vector machine model also demonstrated better performance than logistic regression, with higher accuracy, recall, and F1 index, but a slightly lower AUC. The key variables of sex, elderly, duration of abdominal drainage, preoperative WBC count, and absolute value of neutrophils were found to be strong predictors of delayed wound healing. Patients with longer duration of abdominal drainage had a significantly higher risk of delayed wound healing, with a risk ratio of 1.579 compared to those with shorter duration of abdominal drainage. Similarly, preoperative WBC count, sex, elderly, and absolute value of neutrophils were associated with a higher risk of delayed wound healing, highlighting the importance of these variables in the model.

CONCLUSION

The model is able to identify high-risk patients based on sex, elderly, duration of abdominal drainage, preoperative WBC count, and absolute value of neutrophils can provide valuable insights for clinical decision-making.

Keywords: Machine learning; Logistic regression; Support vector machine; Decision tree; Delayed healing; Prediction model; Gastric cancer

Core Tip: Delayed wound healing after gastric cancer surgery poses a significant risk to patient recovery. This study developed machine learning models specifically decision tree, support vector machine, and logistic regression for predicting postoperative delayed wound healing. The decision tree model demonstrated the best performance, achieving an accuracy of 90.1% and an area under the curve of 0.951 in the validation set. Key predictors included duration of abdominal drainage and preoperative white blood cell count. These findings offer clinicians a data-driven tool for early identification of high-risk patients and improved perioperative management.