Table 1 Main strengths and weaknesses of popular predictive models.

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.

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.

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.

Parsimony (few model parameters); interpretability of the parameters in terms of odds.

Outliers can affect results significantly; certain assumptions about predictors; difficult updating.

Very simple use in clinical practice; strong intuitive appeal; widespread use in heart surgery.

Worse performance than more complex models; difficult customization and updating.

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.