|
Neural Networks
|
Discover non-linear relationships. Can assess multi-level interactions
|
“Black Box” to clinicians; hard to implement into a DSS*
|
|
Random Forests
|
Finds most probable solution set; robust against scaling influences
|
Not always best in terms of prediction; hard to implement into a DSS
|
|
Cluster Analysis
|
Finds groups of very similar patients; exploratory analysis
|
Unsupervised technique
|
|
Principal Components Analysis
|
Uncovers the variables contributing the most to outcome variation
|
Not amenable to binary outcomes; assumes additive relationship
|
|
Support Vector Machines
|
Robust against statistical assumptions
|
Difficult to implement into a DSS
|
