Table 2.
Number and proportion of papers according to the type of machine learning used and number of patients analysed (for prediction studies only)
| Number of patients analysed | |||||||
|---|---|---|---|---|---|---|---|
| Type of machine learning | Number (%) of papers with this typea | < 100 | 100–1000 | 1000–10,000 | 10,000–100,000 | 100,000–1,000,000 | Number not reported |
| Neural network | 72 (42.6%) | 14 (19.4%) | 27 (37.5%) | 20 (27.8%) | 9 (12.5%) | 2 (2.8%) | 0 (0.0%) |
| Support vector machine | 40 (23.7%) | 12 (30.0%) | 15 (37.5%) | 8 (20.0%) | 4 (10.0%) | 1 (2.5%) | 0 (0.0%) |
| Classification/decision trees | 35 (20.7%) | 6 (17.1%) | 11 (31.4%) | 10 (28.6%) | 5 (14.3%) | 1 (2.9%) | 2 (5.7%) |
| Random forest | 21 (12.4%) | 1 (4.8%) | 9 (42.9%) | 5 (23.8%) | 4 (19.0%) | 2 (9.5%) | 0 (0.0%) |
| Naive Bayes/Bayesian networks | 19 (11.2%) | 4 (21.1%) | 5 (26.3%) | 6 (31.6%) | 2 (10.5%) | 1 (5.3%) | 1 (5.3%) |
| Fuzzy logic/rough set | 12 (7.1%) | 3 (25.0%) | 5 (41.7%) | 2 (16.7%) | 1 (8.3%) | 0 (0.0%) | 1 (8.3%) |
| Other techniquesb | 28 (16.7%) | 2 (7.1%) | 10 (35.7%) | 8 (28.6%) | 7 (25.0%) | 1 (3.6%) | 0 (0.0%) |
| Total (accounting for duplicates) | 169 | 37 (21.9%) | 56 (33.1%) | 42 (24.9%) | 26 (15.4%) | 4 (2.37%) | 4 (2.37%) |
aPapers can have more than one approach—percentages may total more than 100
bOther techniques (number of studies): causal phenotype discovery (1), elastic net (1), factor analysis (1), Gaussian process (2), genetic algorithm (1), hidden Markov models (1), InSight (4); JITL-ELM (1), k-nearest neighbour (3), Markov decision process (1), particle swarm optimization (1), PhysiScore (1), radial domain folding (1), sequential contrast patterns (1), Superlearner (4), switching linear dynamical system (1), Weibull-Cox proportional hazards model (1), method not described (2)