METHODS: We use Non-linear Iterative Partial Least Squares to perform the data dimensionality reduction, Self-Organizing Map technique for clustering task and ensembles of Neuro-Fuzzy Inference System for predicting the hepatitis disease. We also use decision trees for the selection of most important features in the experimental dataset. We test our method on a real-world dataset and present our results in comparison with the latest results of previous studies.
RESULTS: The results of our analyses on the dataset demonstrated that our method performance is superior to the Neural Network, ANFIS, K-Nearest Neighbors and Support Vector Machine.
CONCLUSIONS: The method has potential to be used as an intelligent learning system for hepatitis disease diagnosis in the healthcare.
METHODS: An integration of fuzzy logic and decision-making trial and evaluation laboratory (DEMATEL) is utilized, and data was collected from a panel of professional experts in Malaysia. Using a cause-effect relationship diagram, the fuzzy DEMATEL method evaluates the causal relationships between factors.
RESULTS: Findings showed that environmental factors play the most significant roles in preventing COVID-19 infection, followed by technology, individual, and social factors. Getting vaccinated is the most crucial factor in the environmental dimension in cutting the spread of COVID-19. Telehealth, the use of personal protective equipments (PPEs), and the adoption of social distancing are the most important measures in technology, individual and social dimensions, respectively.
CONCLUSIONS: This study offered valuable insights for policymakers and healthcare professionals in designing and implementing effective strategies to prevent pandemic disease transmission. Findings can be practically applied to optimize and prioritize infection prevention measures, assign resources more effectively, and guide evidence-based decision-making in the face of evolving pandemic situations. This process involves the active commitment of all parties, including governments, medical health executives, and citizens.