Key result
A support vector machine (SVM) model combining clinical and laboratory features achieved an AUC of 0.895 in the testing set for preoperatively predicting complicated appendicitis, demonstrating better overall performance than logistic regression and random forest models.
Why the study?
Accurate preoperative prediction of complicated appendicitis could assist in selecting optimal treatment and reducing risks of postoperative complications.
Does a machine learning model based on clinical and laboratory data accurately predict complicated appendicitis preoperatively in patients with acute appendicitis?
Observational (n=136)
No
Does a machine learning model based on clinical and laboratory data accurately predict complicated appendicitis preoperatively in patients with acute appendicitis?
An SVM machine learning model using clinical and laboratory data can accurately predict complicated appendicitis preoperatively, potentially assisting diagnosis in resource-limited settings.
ML model may aid preoperative CA risk stratification; leaves open prospective validation before clinical adoption.
BACKGROUND Accurate preoperative prediction of complicated appendicitis (CA) could help selecting optimal treatment and reducing risks of postoperative complications. The study aimed to develop a machine learning model based on clinical symptoms and laboratory data for preoperatively predicting CA.METHODS 136 patients with clinicopathological diagnosis of acute appendicitis were retrospectively included in the study. The dataset was randomly divided (94: 42) into training and testing set. Predictive models using individual and combined selected clinical and laboratory data features were built separately. Three combined models were constructed using logistic regression (LR), support vector machine (SVM) and random forest (RF) algorithms. The CA prediction performance was evaluated with Receiver Operating Characteristic (ROC) analysis, using the area under the curve (AUC), sensitivity, specificity and accuracy factors.RESULTS The features of the abdominal pain time, nausea and vomiting, the highest temperature, high sensitivity-CRP (hs-CRP) and procalcitonin (PCT) had significant differences in the CA prediction (P<0.001). The ability to predict CA by individual feature was low (AUC<0.8). The prediction by combined features was significantly improved. The AUC of the three models (LR, SVM and RF) in the training set and the testing set were 0.805, 0.888, 0.908 and 0.794, 0.895, 0.761, respectively. The SVM-based model showed a better performance for CA prediction. RF had a higher AUC in the training set, but its poor efficiency in the testing set indicated a poor generalization ability.CONCLUSIONS The SVM machine learning model applying clinical and laboratory data can well predict CA preoperatively which could assist diagnosis in resource limited settings.
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Kang et al. (2020) conducted an observational in Acute appendicitis (n=136). Support vector machine (SVM) predictive model vs. Logistic regression (LR) and Random forest (RF) models was evaluated on Area under the curve (AUC) for predicting complicated appendicitis in the testing set (p=>0.05). A support vector machine (SVM) model combining clinical and laboratory features achieved an AUC of 0.895 in the testing set for preoperatively predicting complicated appendicitis, demonstrating better overall performance than logistic regression and random forest models.
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