Table 1.

Main strengths and weaknesses of popular predictive models.

Model	Strengths	Weaknesses
BL	Easy to construct (quick learning and low computational overhead); low sensitivity to missing data; recursive updating.	Low performance with clearly non-normal data or manifestly non homoscedastic distributions; poor calibration.
BQ	Easy to construct (quick learning and low computational overhead); low sensitivity to missing data; recursive updating.	Low performance with clearly non-normal data; poor calibration.
kNN	Very intuitive; no statistical assumption about the data; good classification if number of samples is large enough.	Critical choice of neighbourhood size and metric; large storage requirements and time consuming for large databases.
LR	Parsimony (few model parameters); interpretability of the parameters in terms of odds.	Outliers can affect results significantly; certain assumptions about predictors; difficult updating.
ISS	Very simple use in clinical practice; strong intuitive appeal; widespread use in heart surgery.	Worse performance than more complex models; difficult customization and updating.
ANN	No statistical assumption about data; ability to estimate non-linear relationships between input data and outputs.	Long training process; experience needed to determine network topology; poor interpretability; difficult updating.

BL, Bayes linear; BQ, Bayes quadratic; kNN, k-nearest neighbour; LR, logistic regression; ISS, integer scoring systems; ANN, artificial neural network.