ABSTRACT
In this paper, we propose a performance assessment of a forest induction method called IDTNIM-RF which uses the IDT_NIM "Induction of Decision Tree New Information Measure" for the trees induction. The specific techniques applied to forests such as bagging and random feature selection are used for generating multiple IDT_NIM trees. We compare the IDTNIM-RF method with a RF random forest that uses CART as a basic rule. The success rate criterion of the single CART and IDT_NIM trees used respectively to generate URF and IDTNIM-RF forest sets is used as a performance assessment basis. To achieve this evaluation, data sets are carried from the UCI Repository and some learning bases we have already developed such MONITDIAB and COVID_EHU.
ABSTRACT
Symptoms associated with COVID-19 are very similar to and difficult to distinguish from those of seasonal flu, bronchitis, or pneumonia. The use of tests, expensive and unavailable in most countries, especially developing ones, may be unnecessary in the case of a suspected COVID. This work is carried out in order to decide if a patient is a priori infected and must be tested. Otherwise, the patient will not be screened using a confidence threshold. The data is collected at the emergency department of the EHU of Oran in Algeria. The COVID-19infection classification and prediction are performed by decision trees. © 2021, Springer Nature Switzerland AG.