Prediction of cardiac disease-causing pattern using multimedia extraction in health ontology

Abstract

For service and multimedia processing that are not limited by time and space, it is necessary to go beyond the existing computing paradigm and resolve such limitations. In this study, health big data-based cardiac disease induction prediction made with multimedia extraction is suggested, which analyzes the relationships in health big data using multimedia extraction. Multimedia extraction is roughly divided into two types: extraction of structured data–based significant items, and extraction of unstructured data–based information. The extraction of structured data–based significant items is made with a multivariate analysis algorithm and similarity analysis. The extraction of unstructured data–based information is made with a technique called parsing based on medical keywords. Using personal health record (PHR)-based data, health big data are collected, while items having significant relationships are selected using logistics regression. Depending on the proximity of the Minkowski distance, a risky group with high similarity to patients with cardiovascular diseases is formed, while risk factors for cardiovascular diseases are evaluated using the similarities between the risky group and the user. A multivariate analysis was used to analyze the items with a significant level of significance. Through this, 27 out of 210 items were extracted. Therefore, only 12.9% of the data are used, and with the MAE results, it was found that an error in accuracy of 0.21. These results show that the suggested model could provide more personalized data and can be used as core technology for constructing an effective, efficient, smart healthcare system.