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The Role of Classification Trees and Expert Knowledge in Building Bayesian Networks: A Case Study in Medicine

In clinical research an early and prompt detection of the risk class of a new patient may really play a crucial role in determining the effectiveness of the treatment and, consequently, achieving a satisfying prognosis of the patient's chances. There exists a number of popular rule-based algorithms for classification, whose performances are very attractive whenever data of large number of patients are available. However, when datasets only include data of a few hundred patients, the most common approaches give unstable results and developing effective decision-support systems become scientifically challenging. Since rules can be derived from different models as well as expert knowledge resources, each of them having its advantages and weaknesses, this article suggests a “hybrid” approach to address the classification problem when the number of patients is too small to effectively use a single technique only. The hybrid strategy was applied to a case study and its predictive performance was compared with performances of each single approach: due to the seriousness of a misclassification of high-risk patients, special attention was paid on the specificity. The results show that the hybrid strategy outperforms each single strategy involved.