A Novel Approach to Dual Feature Selection of Atrial Fibrillation Based on HC-MFS.

This investigation sought to discern the risk factors for atrial fibrillation within Shanghai's Chongming District, analyzing data from 678 patients treated at a tertiary hospital in Chongming District, Shanghai, from 2020 to 2023, collecting information on season, C-reactive protein, hypertension, platelets, and other relevant indicators. The researchers introduced a novel dual feature-selection methodology, combining hierarchical clustering with Fisher scores (HC-MFS), to benchmark against four established methods. Through the training of five classification models on a designated dataset, the most effective model was chosen for method performance evaluation, with validation confirmed by test set scores. Impressively, the HC-MFS approach achieved the highest accuracy and the lowest root mean square error in the classification model, at 0.9118 and 0.2970, respectively. This provides a higher performance compared to existing methods, thanks to the combination and interaction of the two methods, which improves the quality of the feature subset. The research identified seasonal changes that were strongly associated with atrial fibrillation (pr = 0.31, FS = 0.11, and DCFS = 0.33, ranked first in terms of correlation); LDL cholesterol, total cholesterol, C-reactive protein, and platelet count, which are associated with inflammatory response and coronary heart disease, also indirectly contribute to atrial fibrillation and are risk factors for AF. Conclusively, this study advocates that machine-learning models can significantly aid clinicians in diagnosing individuals predisposed to atrial fibrillation, which shows a strong correlation with both pathological and climatic elements, especially seasonal variations, in the Chongming District.

A novel general-purpose hybrid model for time series forecasting.

Realizing the accurate prediction of data flow is an important and challenging problem in industrial automation. However, due to the diversity of data types, it is difficult for traditional time series prediction models to have good prediction effects on different types of data. To improve the versatility and accuracy of the model, this paper proposes a novel hybrid time-series prediction model based on recursive empirical mode decomposition (REMD) and long short-term memory (LSTM). In REMD-LSTM, we first propose a new REMD to overcome the marginal effects and mode confusion problems in traditional decomposition methods. Then use REMD to decompose the data stream into multiple in intrinsic modal functions (IMF). After that, LSTM is used to predict each IMF subsequence separately and obtain the corresponding prediction results. Finally, the true prediction value of the input data is obtained by accumulating the prediction results of all IMF subsequences. The final experimental results show that the prediction accuracy of our proposed model is improved by more than 20% compared with the LSTM algorithm. In addition, the model has the highest prediction accuracy on all different types of data sets. This fully shows the model proposed in this paper has a greater advantage in prediction accuracy and versatility than the state-of-the-art models. The data used in the experiment can be downloaded from this website: https://github.com/Yang-Yun726/REMD-LSTM.

Influence of polyethylene terephthalate/ polyamide (PET/PA) bilayer structure on physical and strength-related properties of superpressure angioplasty balloons.

BACKGROUND: Percutaneous transluminal coronary angioplasty (PTCA), including balloon angioplasty, is a standard clinical invasive treatment for coronary artery disease. The coronary lesion with calcification is difficult to dilate and the prevention of balloon failure is especially important. OBJECTIVE: A novel superpressure balloon was fabricated with bilayered structure of polyethylene terephthalate (PET) and PA12 (polyamide). METHODS: The structures of bilayer balloon were adjusted to achieve overall excellent performance. Physicochemical, thermal and mechanical properties of bilayer balloons were characterized by X-ray diffraction, differential scanning calorimeter, hydraulic tester and theoretical simulation. RESULTS: Compared with pure PA12 and PET balloons, PA12 outer layer: PET inner layer balloon with layer ratio of 3:7 shows the highest burst stress and relatively low compliance due to an increase in crystallinity and orientation. CONCLUSIONS: The produced bilayer balloon proved to possess a small folding dimension thanks to its ultrathin bilayer structure, which is good for crossing cramped vessels. We believe these optimally fabricated bilayer balloons are proved to provide attractive opportunities for preparation, performance enhancement, and practical applications of super-pressure balloon catheters and cryoablation balloons, that will significantly promote the development of percutaneous transluminal coronary angioplasty for clinical applications.