| Autor: |
Chowdhury SU; Department of Computer Science and Engineering, Brac University, 66 Mohakhali, Dhaka 1212, Bangladesh., Sayeed S; Department of Computer Science and Engineering, Brac University, 66 Mohakhali, Dhaka 1212, Bangladesh., Rashid I; Department of Computer Science and Engineering, Brac University, 66 Mohakhali, Dhaka 1212, Bangladesh., Alam MGR; Department of Computer Science and Engineering, Brac University, 66 Mohakhali, Dhaka 1212, Bangladesh., Masum AKM; Department of Computer Science and Engineering, International Islamic University Chittagong, Chittagong 4318, Bangladesh., Dewan MAA; School of Computing and Information Systems, Athabasca University, 1 University Dr, Athabasca, AB T9S 3A3, Canada. |
| Abstrakt: |
Dengue is a viral disease that primarily affects tropical and subtropical regions and is especially prevalent in South-East Asia. This mosquito-borne disease sometimes triggers nationwide epidemics, which results in a large number of fatalities. The development of Dengue Haemorrhagic Fever (DHF) is where most cases occur, and a large portion of them are detected among children under the age of ten, with severe conditions often progressing to a critical state known as Dengue Shock Syndrome (DSS). In this study, we analysed two separate datasets from two different countries- Vietnam and Bangladesh, which we referred as VDengu and BDengue, respectively. For the VDengu dataset, as it was structured, supervised learning models were effective for predictive analysis, among which, the decision tree classifier XGBoost in particular produced the best outcome. Furthermore, Shapley Additive Explanation (SHAP) was used over the XGBoost model to assess the significance of individual attributes of the dataset. Among the significant attributes, we applied the SHAP dependence plot to identify the range for each attribute against the number of DHF or DSS cases. In parallel, the dataset from Bangladesh was unstructured; therefore, we applied an unsupervised learning technique, i.e., hierarchical clustering, to find clusters of vital blood components of the patients according to their complete blood count reports. The clusters were further analysed to find the attributes in the dataset that led to DSS or DHF. |
