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.

对于不受时间和空间限制的服务和多媒体处理，有必要超越现有的计算范式并解决此类限制。在这项研究中，建议使用多媒体提取进行基于健康大数据的心脏病诱发预测，从而使用多媒体提取分析健康大数据中的关系。多媒体提取大致分为两种类型：基于结构化数据的重要项目的提取和基于非结构化数据的信息的提取。使用多变量分析算法和相似性分析提取基于结构化数据的重要项目。基于非结构化数据的信息的提取是通过称为解析的技术进行的根据医学关键词。使用基于个人健康记录（PHR）的数据，收集健康大数据，而使用物流回归选择具有重要关系的项目。取决于Minkowski的接近程度距离远的地方，形成了与心血管疾病患者高度相似的危险人群，而心血管疾病的危险因素则根据危险人群与使用者之间的相似性进行评估。使用多变量分析来分析具有重要意义的项目。这样，从210个项目中提取了27个。因此，仅使用了12.9％的数据，使用MAE结果，发现精度误差为0.21。这些结果表明，所建议的模型可以提供更多的个性化数据，并且可以用作构建有效，高效，智能医疗系统的核心技术。