Multimodal Neuro-Fuzzy Approach for Emergency Healthcare Virtual Infrastructure

The Multimodal Neuro-Fuzzy Approach (MNFA) is a novel approach to providing healthcare services through virtual infrastructure. This method combines Artificial Intelligence (artificial neural networks) techniques with fuzzy logic, to provide a robust and reliable virtual healthcare infrastructure in cases of emergency. The MNFA approach enables healthcare professionals to have access to up-to-date medical information and treatment guidelines quickly and accurately even in remote or rural areas. Therefore, this approach has the potential for providing efficient and effective healthcare services in remote locations which otherwise would not be accessible due to geographical constraints. Additionally, with the help of MNFA approach, healthcare organizations can save time and money by reducing manual labor costs associated with responding to emergencies. This allows for better automation of clinical decisions in emergency situations. The MNF model is already trained with help azure of Microsoft and gets accuracies in matlab (ANFIS). It enables faster and more accurate diagnosis, improved patient care coordination, and a better understanding of the dynamics between health care providers and patients. This approach can also be used to design intelligent systems that are capable of accurately predicting patient outcomes based on past data. As created a framework with Django and MySQL which is deploy on cloud platform to easy access of framework and collect more data on which will store in cloud database and present in cloud public domain. As such, it provides an invaluable tool for improving healthcare services in times of crisis. Healthcare professionals to effectively monitor, analyze, and take necessary action in an emergency situation. In this framework use cloud platform to fast and easy access of virtual healthcare. This experimental investigation demonstrates that, when used to solve real-world problems, our suggested approach outperforms alternative methods.