A Case-Based-Reasoning System for Feature Selection and Diagnosing Asthma

Abstract

Asthma is a chronic disease of the airways in the lungs. The differentiation between asthma, COPD and bronchiectasis in the early stage of disease is very important for the adoption of appropriate therapeutic measures. In this research, a case-based-reasoning (CBR) model is proposed to assist a physician to therapy. First of all, features and symptoms are determined and patients' data is gathered with a questionnaire, then CBR algorithm is run on the data which leads to the asthma diagnosis. The system was tested on 325 asthmatic and non-asthmatic adult cases and the accuracy was eighty percent. The consequences were promising. This study was performed in order to determine risk factors for asthma in a specific society and the results of research showed that the most important variables of asthma disease are symptoms hyper-responsive, frequency of cough and cough.

Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction

“…Furthermore, larger datasets should commonly be splitted using crossvalidation to approximate external validity thus generate better outcomes [68]. Simultaneously, findings need to be transferred to other types of diseases, for example, different types of cancer [67,87,[111][112][113]118], but also to other clinical application [100,114,115,117]. In addition, one must ask why scientific evidence is not yet widely integrated into disease diagnostics, for instance, in hospitals or other clinical environments.…”