Table 3 Virtues and limitations of the various ML strategies
ML strategy | Virtues | Limitation | Preferred Diagnoses | Reference |
|---|---|---|---|---|
Supervised Learning | - Assists in resolving issues with training data - Provides results with good performance measures - Task driven approach - Classification and Regression | - Training data must be labeled - Input data must be of good quality with adequate data | Pneumonia | [91] |
Unsupervised Learning | - It works best with unprocessed or raw data - Data driven approach - Clustering and Dimensionality Reduction | - Does not employ a feedback mechanism to evaluate the standard results | Lung Cancer | [92] |
Semi-supervised Learning | - Data with labels and without labels can both be used - Classification and Clustering | - Unable to handle unobserved data | COVID-19 | [11] |