Published Paper Details:

most important issues in society. The widespread use of technology in the healthcare industry has led to the growth of electronic data. With vast amounts of data, physicians face the challenge of accurately analyzing symptoms and recognizing disease early. However, supervised machine learning (ML) algorithms have helped medical professionals predict high-risk diseases early. A disease is a specific abnormal condition that adversely affects the structure or function of living organisms. It is very important to know early if you have the disease instead of discovering it later. In this way, a disease prediction system that predicts disease from symptoms plays an important role. The disease prediction system uses a machine learning algorithm called XG-Boost. In doing so, it predicts different kinds of diseases. Each disease presents a person with different signs and symptoms. This paper reviews various models based on such algorithms and techniques and analyzes their performance Models based on supervised learning algorithms such as Support Vector Machines, Gradient Boosting Classifier, Logistic regression, K- Nearest Neighbors (KNN), Convolutional Neural Networks (CNN), Naive Bayes, Decision Tree, and Random Forest are found very popular among the researches.