Fig. 1 Ranking of variable
importance of RSI in Random Forest
model.
Feature selection andclinical signature
development
The clinical feature variables
included a total of 32 categories (refer to section 2.2 for details). To
construct clinical features (CLI), we
followed the same method as in the previous section. Firstly, six
machine learning algorithms, including Gradient Boosting, Support Vector
Machine, AdaBoost, Random Forest, K-Nearest Neighbor, and Neural
Network, were employed, and the results indicated that the Support
Vector Machine model was better prediction results (Table 3).
The Support Vector Machine model boasted a prediction accuracy of 0.8627
(95% CI: 0.8078-0.9068). To extract the corresponding CLI, the Support
Vector Machine algorithm was implemented on the clinical data of each
patient. The importance variables and ranking results of features in the
Support Vector Machine model were revealed in Fig. 2. Subsequently, the
Support Vector Machine algorithm was utilized to extract the
corresponding CLI from the clinical data of each patient.
Table 3 Machine learning outcomes of CLI.