Prediction Of Heart Disease and Identification of Severity Level Using Machine Learning Classifier with Android App

Abstract

heart diseases (HD) become one of the most dangerous diseases in the world today and the primary reason for death. In the United States, heart disease is one of the leading causes of mortality for men, women, and members of the majority of racial and ethnic groups. Despite being technologically advanced, America has yet to discover a cure for this deadly disease. Early spotting of people having heart disease may reduce the rate of disease and the mortality rate in a population. In this paper, a smart Android app using machine learning classifiers is proposed for the early detection of heart disease and the determination of its severity level. The proposed android application consists of 7 different aspects for heart disease prediction including login, signup, home, requirement, result, view, and prevention activity. Initially, the patient’s clinical data is gathered, examined, and correlated with their risk for developing clinical symptoms that could indicate heart disease. The application classifies the user's heart disease risk as high, low, or medium based on the risk factors they enter. Analyzing and correlating the data discovered a significant correlation between having heart disease and the application results in the high & low, medium & low, and medium & high categories. K-Nearest Neighbor, Logistic Regression, Random Forest, Decision Tree, Ada Boost, Gradient Boosting, and XGBoost Algorithm are used to classify heart disease. The proposed application obtains the training accuracy 92.0, testing accuracy 87.0, precision 87.0, recall 87.0 and F1-Score 87.0 for Gradient Boosting. This study aims to examine the efficiency of mobile technology in the early risk detection of heart disease.