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