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Objective: In the application of machine learning to the prediction of hypertension, many factors have seriously affected the classification accuracy and generalization performance. We propose a pulse wave classification model based on multi-feature fusion for accuracy prediction of hypertension., Methods and Materials: We propose an ensemble under-sampling model with dynamic weights to decrease the influence of class imbalance on classification, further to automatically classify of hypertension on inquiry diagnosis. We also build a deep learning model based on hybrid attention mechanism, which transforms pulse waves to feature maps for extraction of in-depth features, so as to automatically classify hypertension on pulse diagnosis. We build the multi-feature fusion model based on dynamic Dempster/Shafer (DS) theory combining inquiry diagnosis and pulse diagnosis to enhance fault tolerance of prediction for multiple classifiers. In addition, this study calculates feature importance ranking of scale features on inquiry diagnosis and temporal and frequency-domain features on pulse diagnosis., Results: The accuracy, sensitivity, specificity, F1-score and G-mean after 5-fold cross-validation were 94.08%, 93.43%, 96.86%, 93.45% and 95.12%, respectively, based on the hypertensive samples of 409 cases from Longhua Hospital affiliated to Shanghai University of Traditional Chinese Medicine and Hospital of Integrated Traditional Chinese and Western Medicine. We find the key factors influencing hypertensive classification accuracy, so as to assist in the prevention and clinical diagnosis of hypertension., Conclusion: Compared with the state-of-the-art models, the multi-feature fusion model effectively utilizes the patients' correlated multimodal features, and has higher classification accuracy and generalization performance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The application of deep learning to the classification of pulse waves in Traditional Chinese Medicine (TCM) related to hypertensive target organ damage (TOD) is hindered by challenges such as low classification accuracy and inadequate generalization performance. To address these challenges, we introduce a lightweight transfer learning model named MobileNetV2SCP. This model transforms time-domain pulse waves into 36-dimensional frequency-domain waveform feature maps and establishes a dedicated pre-training network based on these maps to enhance the learning capability for small samples. To improve global feature correlation, we incorporate a novel fusion attention mechanism (SAS) into the inverted residual structure, along with the utilization of 3 × 3 convolutional layers and BatchNorm layers to mitigate model overfitting. The proposed model is evaluated using cross-validation results from 805 cases of pulse waves associated with hypertensive TOD. The assessment metrics, including Accuracy (92.74 %), F1-score (91.47 %), and Area Under Curve (AUC) (97.12 %), demonstrate superior classification accuracy and generalization performance compared to various state-of-the-art models. Furthermore, this study investigates the correlations between time-domain and frequency-domain features in pulse waves and their classification in hypertensive TOD. It analyzes key factors influencing pulse wave classification, providing valuable insights for the clinical diagnosis of TOD., Competing Interests: Declaration of competing interest None declared., (Copyright © 2024 IPEM. Published by Elsevier Ltd. All rights reserved.)

• 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. [ABSTRACT FROM AUTHOR]

H-type hypertension increases the morbidity of stroke and cardiovascular diseases, posing a great threat to human health. The existing diagnosis of H-type hypertension detects patients' plasma homocysteine content, which is inefficient and leaves wounds. The pulse-taking diagnosis of traditional Chinese medicine (TCM) can implement the non-invasive auxiliary diagnosis of H-type hypertension by the physiological activities of patient's pulse wave combined with the clinical inquiry information. Therefore, we propose a combined model of pulse-taking and inquiry diagnosis, which includes a pulse-taking model based on CNN-BiLSTM and an inquiry diagnosis model based on the integrated Cluster-RFs. We search the optimal weights for the heterogeneous integrated model dynamically by using Grid Search to classify H-type hypertension. The datasets are the collection of 364 cases H-type hypertension 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 indicator of classification of H-type hypertension, i.e. Sensitivity, Specificity, Accuracy, F1-score, AUC, is 82.31%, 73.75%, 79.34%, 85.92%, 87.78% respectively, is higher than that of the other typical machine learning models on pulse-taking or inquiry diagnosis. In addition, this article also studies the correlation between pulse-taking or inquiry diagnosis and its attributes, and analyzes the feature importance ranking on pulse-taking and inquiry diagnosis, which aids clinicians seek the occurrence mechanisms of H-type hypertension, and find the reasonable measurements for timely prevention and treatment. [ABSTRACT FROM AUTHOR]