Journal of Clinical Psychiatry vol:76 issue:10 pages:1292-1299
Metabolic and cardiovascular diseases in patients with schizophrenia have gained a lot of interest in
recent years. Developing an algorithm to detect the metabolic syndrome based on readily available
variables would eliminate the need for blood sampling, which is considered as expensive and
inconvenient in this population.
We used logistic regression and optimized artificial neural networks and support vector machines to
detect the metabolic syndrome in a cohort of schizophrenia patients of the UPC Kortenberg, KU
Leuven. Testing was done on one third of the included cohort (202 patients), training was performed
using a tenfold stratified cross-validation scheme.
All three methods yielded similar results with satisfying accuracies of about 80 %. However, none of
the advanced statistical methods could improve on the results obtained using a very simple and
naïve model including only central obesity and information on blood pressure.
Although so-called patter recognition techniques bear high promise in improving clinical decision
making, the results should be presented with caution and preferably in comparison with some lowtech