Application of Evidential Reasoning rules to identification of asthma control steps in children

Abstract

The UK is one of these countries in Europe, which have the highest death rate from asthma, and the rules to identify asthma control steps are vague in the current official guideline of asthma management. In this research, diagnosis rules on asthma control steps are developed to supplement the current guideline and to assist patients to monitor and manage their asthma on daily basis. The main challenge of developing the rules is missing values. Although the data examined have prodigious volumes of records for patients, no one have all and different patients have different information recorded. The large proportion of missing values lead to comparatively limited powers of some techniques like Decision Tree Analysis, Logistic Regression, ANN, Bayes' Rule and SVM. This research explores the Evidential Reasoning (ER) rule to develop prognostic rules for asthma control steps. ER is prior-free probabilistic inference and has not been applied to disease diagnosing and monitoring. The results are represented as probability distributions on asthma control steps given any combination of evidence, even if some combinations are not recorded in the current database. In practice, it could help clinicians to identify asthma control steps of patients, prescribe corresponding treatments, and monitor the effectiveness of the treatment and the progress of patients in asthma control management.