Table 1 Overview of advantages and disadvantages of commonly utilized machine learning algorithm clustered by utilized type of learning
From: The machine learning horizon in cardiac hybrid imaging
Learning Type | Purpose | Algorithm | Advantages | Disadvantages |
|---|---|---|---|---|
Unsupervised | Clustering | K-means | • Intuitive algorithm with linear relative computational complexity | • Number of clusters or classes must be defined by user • Poor performance with clusters of irregular shapes |
Mean Shift | • No need to select number of clusters or classes | • Utilized window size must be defined by user | ||
DBSCAN | • No need to select number of clusters or classes • It can identify points as noise and cluster with irregular shapes | • Windows size must be defined by user • Reduced performance when clusters have different densities | ||
Expectation-Maximization | • It can identify clusters with ellipsoidal shapes • It assigns membership probabilities to each point | • Number of clusters or classes must be defined by user | ||
Supervised | Classification | Logistic Regression | • Good performance with small datasets • Its output can be interpreted as a probability | • Data assumptions are needed to be complied • It can only provide linear solutions |
K-Nearest Neighbors | • Intuitive algorithm | • Number of neighbors must be defined by user • High relative computational complexity | ||
Naive Bayes | • Performs well in small datasets if conditional independent assumption holds | • Assumption of independence between features | ||
Support Vector Machines | • It can provide non-linear solutions | • To achieve good performance, they require knowledge about the kernel employed | ||
Decision trees ensembles | • They can handle categorical features • Few parameters to tune • They perform well in datasets with large number of features | • Interpretability of ensemble can be questioned | ||
Neural Networks | • State-of-the-art results • Direct complex image processing | • Many parameters to fine-tune • Large number of samples are required to achieve good performance | ||
Regression | Linear (LASSO, Ridge) | • Good performance with small datasets | • Data assumptions are needed to be complied • Can only provide linear solutions | |
Decision trees | • They can provide non-linear solutions | • Interpretability of ensemble can be questioned | ||
Segmentation | U-Net | • One stage algorithm with good performance and variants |  | |
Mask-RCNN | • State-of-the-art performance | • Two-stage algorithm |