Emergency Medical Dispenser
Automation is a field that deals and overlaps with electronics, artificial engineering and computer science, engineering. Collaborative services of doctors and machines have always been a great challenge in today’s world. The dataset for this model is collected through a survey. A model well trained on this data is seen as a good fit that could be then used to serve medical assistance to people using cloud and IoT. By periodically collecting patient’s clinical data on their own and transferring them to physicians located in remote sites allows patient’s health status regulation and response provision in a better way. In this model we are using Naive Bayes machine Learning algorithm for diagnosing. This type of telemedicine system guarantees patient control while reducing costs. The proposed system will be very useful in rural primary health care centres. Proposed system architecture is based on the combination of, web services, and the autonomic computing paradigm to manage data in home-based tele monitoring scenarios.