SVM and Formal Concept Analysis for epidemics detection
Salah Zidi · 2020 International Conference on Computing and Information Technology (ICCIT-1441) · 2020
In this paper, a new classification system is proposed for the epidemic risk management. It consists of a decision support system using an expertise database shared between several hospitals. The goal of this system is to assist medical professionals in epidemic situation detection making pervasive decisions. A semi-supervised classification approach is proposed. It is a hybrid algorithm using Support Vector Machines (SVM) as a supervised classification approach, k-means as a non-supervised approach and Formal Concept Analysis (FCA) as a features selection technique.