Table 1.
Algorithm
Advantage
Limitation
Example application in musculoskeletal ultrasonography
Logistic regression
Provides probabilistic interpretation of model parameters
Only used to predict discrete function
-
Quick model update for incorporating new data
Sensitive to outliers
K-nearest neighbors
Nonparametric model
Time-consuming and computationally expensive
Nerve identification [10]
Used both for classification and regression problems
Number of neighbors must be defined in advance
Low interpretability
Naïve Bayes
Suitable for relatively small datasets
Classes must be mutually exclusive
-
Handles both binary and multi-class classification problems
Presence of dependency between attributes results in loss of accuracy
Fast application and high computational efficiency
Assumptions such as the normal distribution might be invalid
Support vector machines
Good prediction performance in different tasks
Have "black box" characteristics
Lumbar spine classification [11]
Can handle multiple feature spaces
Sensitive to manual parameter tuning and kernel choice
Synovitis grading [12]
Nerve identification [10]
Decision trees
Perform in datasets with large number of features
Only axis-aligned rectangle splits.
Nerve identification [10]
Few parameter tuning
Inadequate for regression and continuous value prediction problems
High representational power and easy to interpret
Mistake in higher labels cause errors in subtrees
Random forest
Provide estimates of variable or attribute importance in the classification
Complex and computationally expensive
Myositis classification [13]
Ensemble-based classifications shows relatively good performance
Number of base classifiers needs to be defined
Hip 2-D US adequacy classification [14]
Overfitting has been observed for noisy data
Neural networks
Direct image processing
Have "black box" characteristics
Nerve identification [10]
Can map complex nonlinear relationships between dependent and independent variables
Have to fine-tune many parameters
Require a large well-annotated dataset to achieve good performance
K-means
Can process large datasets
Number of clusters must be defined
Nerve localization [15]
Algorithm that is simple to understand and implement