Smart Health Prediction

COMPUTER SCIENCE

The "Smart Health Prediction Using Machine Learning" system, which is based on predictive modelling, is able to anticipate the disease of patients or users based on the symptoms that the user supplies as an input to the system. This is done using the basis of the "Smart Health Prediction Using Machine Learning" system's name, which stands for "Smart Health Prediction Using Machine Learning." The system will then establish a diagnosis based on these symptoms in order to determine whether or not a disease is present. This application provides users with a total of three different login options to choose from: user/patient login, doctor login, and admin login.The user (or patient) enters their symptoms into the device, which then does an analysis of those symptoms and outputs the probability that the disease is present based on the prediction given by the algorithm. This allows the device to assist in the diagnosis process. The Naive Bayes Classifier is applied so that precise forecasts regarding health issues can be created. This allows for better patient care. The Naive Bayes Classifier computes the disease's % likelihood of occuring by taking into consideration all of its attributes. These properties are trained during the training phase.The correct interpretation of disease data enables early disease prediction for the patient or user and provides the user with a clearer perspective on the disease.

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