AI-Driven E-Healthcare Prediction and Monitoring: Leveraging Big Data Intelligence for Immediate Impact

Abstract

Smart diagnosis has been used successfully in a variety of healthcare applications over the last decade. Early detection and diagnosis of heart disease is critical; heart patients can be treated very effectively if they are treated before they suffer a heart attack. Researchers in this field have recently introduced a number of deep learning-based systems for predicting and diagnosing heart disease. However, because of the lack of an intelligent framework that can use multiple sources of data to predict heart disease, these systems are incapable of handling high-dimensional data sets. In this paper, an intelligent health monitoring / prediction model is proposed to predict Cardiovascular disease using modern AI algorithms, which aids in correctly diagnosing heart disease and attempting to reduce the expected error rate. For training the proposed model, a dataset is prepared here, and modern AI algorithms such as K-NN, DT, NB, MLP Classifier, Feed forward ANN, and CNN were used. The proposed system has demonstrated reliability, with an accuracy of 91.27% using the convolutional NN. As a result, the proposed model can be used in an intelligent healthcare system as a disease diagnosis tool.