Predictive Model for Emergency Medical Services (EMS) using Machine Learning and Data Mining

In Emergency Medical Services (EMS), we use Predictive modeling which is basically a statistical technique which anticipates and forecasts future occurrences using machine learning and data mining. Predictive modeling processes current and historical data acquired at a medical treatment using statistical methodologies, data mining, and other techniques. It allows for the formulation of most effective therapeutic regimens personalized for every patient or individual by enhancing client care depending on individual patient data. In the event of hospitalization and emergency care, where quick choices are required, prognostic models are quite useful. The overall purpose of these advanced procedures like regression modeling, parametric modeling and simulation is to improve problem solving, decision making and finding potential for healthcare. In this paper, Data Science and Machine Learning techniques and algorithms are used to create predictive model of the different obtained datasets. As first responders to life-threatening incidents, EMS plays a critical role in raising survival rates. Unbalanced EMS supply-demand distribution in the city, on the other hand, may result in a scarcity of available EMS services and postpone emergency care.