COPD多维特征提取与集成诊断方法 postprint

Abstract: Chronic obstructive pulmonary disease (COPD) is a chronic lung disease that can lead to a gradual decline in respiratory function. Therefore, big data analysis and algorithms are needed to help doctors diagnose diseases more accurately. At present, there are limitations to the study of COPD: On the one hand, the research results only use data to analyze the impact of single features on the disease; on the other hand, the research results are only verified by simple algorithm models for case data. Therefore, this paper proposes a COPD multi-dimensional feature extraction and integrated diagnosis method. First, the MDF-RS algorithm is proposed to extract the optimal combination of multi-dimensional features. Secondly, the DSA-SVM integrated model is proposed to construct the classifier for diagnosis and prediction. Finally, the cross-validation method is used to verify the accuracy and other performance indicators. The experimental comparison shows the effectiveness of the proposed algorithm.