Prediction of symptoms and diseases using machine learning algorithm for health care recognition system

As a result of their surroundings and lifestyle choices, people nowadays are affected by a wide range of ailments. As a result, early disease prediction becomes crucial. Doctors, on the other hand, struggle to make accurate forecasts based on symptoms. Accuracy anticipating disease is the most challenging challenge. Machine learning plays a key part in anticipating in order to solve this difficult issue. To tackle this problem, machine learning plays a key role in disease prediction. Medical science creates vast amounts of data every year. Early patient care has benefited from the good medical data analysis because of the growing volume of data in the medical as well as healthcare professions Disease data are used in data mining to uncover new pattern information in massive volumes of medical data. Based on the clinical condition, we created a broad illness prognosis. We apply the machine learning techniques Gated Recurrent Unit (GRU) and ANFIS to accurately forecast sickness. The collecting of disease symptoms is required for disease prediction. For an accurate prognosis, this general disease prediction considers the person's lifestyle and checkup information.