PMC:4289553 / 2324-3201 JSONTXT

{"target":"http://pubannotation.org/docs/sourcedb/PMC/sourceid/4289553","sourcedb":"PMC","sourceid":"4289553","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4289553","text":"Prediction of binary outcomes is important in medical research. The interest in the development, validation, and clinical application of clinical prediction models is increasing [1]. Most prediction models are based on logistic regression analysis (LR), but other, more modern techniques, may also be used. Support vector machines (SVM), neural nets (NN) and random forest (RF) have received increasing attention in medical research [2–6], since these hold the promise of better capturing non-linearities and interactions in medical data. The increased flexibility of modern techniques implies that larger sample sizes may be required for reliable estimation. Little is known, however, about the sample size that is needed to generate a prediction model with a modern modelling technique that outperforms more traditional, regression-based modelling techniques in medical data.","tracks":[{"project":"2_test","denotations":[{"id":"25532820-16271675-11953994","span":{"begin":434,"end":435},"obj":"16271675"},{"id":"25532820-9819841-11953994","span":{"begin":434,"end":435},"obj":"9819841"},{"id":"25532820-24950066-11953994","span":{"begin":434,"end":435},"obj":"24950066"}],"attributes":[{"subj":"25532820-16271675-11953994","pred":"source","obj":"2_test"},{"subj":"25532820-9819841-11953994","pred":"source","obj":"2_test"},{"subj":"25532820-24950066-11953994","pred":"source","obj":"2_test"}]}],"config":{"attribute types":[{"pred":"source","value type":"selection","values":[{"id":"2_test","color":"#adec93","default":true}]}]}}