Prediction of pulse wave of target organ damage in hypertension based on multiple feature fusion

•An improved feature extraction method of pulse wave in frequency domain is proposed to enhance the discriminability of pulse wave features.•A random forest model based on cluster and under-sampling is proposed to reduce the impact of class imbalance on classification performance for inquiry diagnosis samples.•A fusion attention mechanism model is proposed, which fuses heterogeneous feature maps of pulse-taking and inquiry diagnosis for classification based on feature-level fusion via two-diagnosis integration.•In addition, we also study feature importance ranking of pulse-taking and inquiry diagnosis, analyze the crucial factors affecting classification of hypertension, find the pathogenesis of hypertensive target organ damage, and provide an effective basis for clinical diagnosis. With respect to less efficiency and low accuracy of predicting on hypertensive target organ damage, this article proposes a fusion prediction model combining pulse-taking with inquiry diagnosis of traditional Chinese medicine to accomplish the efficient and non-invasive diagnosis. Regarding the class imbalance of inquiry diagnosis samples, an Eliminated random forest algorithm is proposed to select efficient features and reduce the impact of class imbalance on classification performance via cluster-based under-sampling algorithm. As to low discriminability of hypertensive time-domain pulse wave samples, time-domain pulse wave is transformed to the frequency-domain MFCC feature maps, and fuse feature maps of inquiry diagnosis scale for predicting hypertension target organ damage. In the article, the clinical 608 cases of hypertensive target organ damage are from Longhua Hospital affiliated to Shanghai University of Chinese Medicine and Hospital of Integrated Traditional Chinese and Western Medicine concerning pulse-taking and inquiry diagnosis. The evaluation indicators of 5-Fold cross-validation classification, i.e. F1-score, Accuracy, Precision, Sensitivity, AUC, are 97.31%, 98.72%, 97.71%, 97.04%, 99.13% respectively, which are higher than those of the other typical models. In addition, this article also studies the correlation between classification of pulse-taking or inquiry diagnosis and its features, and analyzes the feature importance ranking on pulse-taking and inquiry diagnosis, which aids clinicians to seek the occurrence mechanisms of hypertensive target organ damage, and find the effective measurements for timely prevention and treatment.