Intelligent Processing and Classification of Multisource Health Big Data from the Perspective of Physical and Medical Integration

Abstract

With the development of computer science and information technology, human society is gradually stepping into the Internet and big data. The medical and health industry can realize the integration and readjustment of existing resources, improve the operation efficiency of the industry, and tap the huge potential of the industry with the support of big data technology. However, the medical data in the new era has the characteristics of massive, high latitude, complex structure, and complex information, which is not conducive to the direct classification of health data. The preprocessing of health data can improve the quality of dataset, reduce the size of data, and improve the efficiency and accuracy of data classification. Based on this and according to the characteristics of health dataset and the existing pretreatment technology, this paper analyzes and improves the algorithm of abnormal data detection and data protocol in the process of reprocessing data cleaning. This paper analyzes and studies feature selection algorithms based on Bayesian inference algorithm and focuses on feature selection algorithms based on random forest. In order to solve the problem that the original algorithm ignored the relationship between the importance degrees of each feature in a single tree, a feature importance degree calculation method based on local importance degree was proposed. Through experimental analysis and comparison, the improved algorithm can select better feature subset and improve the performance of the classification model. Then, TAN classifier, BAN classifier, and MBN classifier were constructed based on preprocessed hypothyroidism data, and the performances of these four classifiers were compared through experiments. The final results show that BAN classifier has the best average classification effect.