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Generating and Verifying Risk Prediction Models using Data Mining
Abstract
Risk prediction models are of great interest to clinicians. They offer an explicit and repeatable means to aide the selection, from a general medical population, those patients that require a referral to medical consultants and specialists. In many medical domains, including cardiovascular medicine, no gold standard exists for selecting referral patients. Where evidential selection is required using patient data, heuristics backed up by poorly adapted more general risk prediction models are pressed into action, with less than perfect results. In this study, existing clinical risk prediction models are examined and matched to the patient data to which they may be applied using classification and data mining techniques, such as neural nets. Novel risk prediction models are derived using unsupervised cluster analysis algorithms. All existing and derived models are verified as to their usefulness in medical decision support on the basis of their effectiveness on patient data from two UK sites.
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