Table 5.

Advantages and disadvantages of the most common algorithms used in LTBI differential diagnostic models

Algorithm	Advantages	Disadvantages
Support vector machine (SVM)	Good generalization ability for high dimensional and nonlinear problems; Can adapt to different data types by selecting different kernel functions; Performs well with a small amount of data	Long training time for large-scale datasets; Challenging to select the appropriate kernel function and parameters for noisy data and nonlinear problems; Does not provide direct probability estimates
Decision trees	Easy to understand and interpret; Can handle nonlinear features and large-scale data; Suitable for both classification and regression problems; Minimal data preprocessing is required	Prone to overfitting, especially with deep trees; Performs poorly with continuous and highly correlated features
Random forest	High accuracy; Can handle high-dimensional and large-scale datasets; Robust to noise and missing data; Provides feature importance estimation	A more complex model with longer training time; Substantial memory consumption for datasets with large feature spaces; Less effective for highly correlated features
Logistic regression	Simple and fast computation; Interpretable parameter weights to understand feature importance; Suitable for binary classification problems	Performs poorly with nonlinear relationships in the data; Prone to underfitting; May not perform well with high-dimensional data or highly correlated features
Hierarchical clustering	No need to specify the number of clusters in advance; Provides a hierarchical structure of clusters; Works with numerical and categorical data; Allows for visual analysis through dendrograms	High computational complexity; Difficulty with high-dimensional data; Restrictions on data types; Irreversible clustering results