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In general, supervised learning has the problem that the learning performance varies depending on the method of organizing the training data pair and the learning structure. Therefore, when applied to real-world applications, learning and recognition performance are reduced. Therefore, this paper proposes an FCM-based associative memory method to improve the classification performance of binary data. The proposed method applies FCM clustering in the input and middle layers, and linear association memory algorithms in the middle and output layers. To evaluate the performance of the proposed FCM clustering-based associative memory algorithm, we will apply the Wisconsin breast cancer dataset. In breast cancer data sets, the diagnosis is binary discriminatory, which classifies malignant and benign. The goal of the experiment was to derive malignancy, and as a result of applying a total of 569 data through 5 K-Fold cross-tests, it was confirmed that the average accuracy was 85.548% and the average F1-score was 72.218. [ABSTRACT FROM AUTHOR]

Multimodal refers to the combination or integration of data from multiple sources or modalities. Multimodal data integration involves merging diverse sources, such as imaging, clinical, and genetic data, to obtain a more comprehensive view of patient health. This process, facilitated by deep learning, has emerged as a transformative technique in the biomedical field, enabling a comprehensive understanding of patient health and leading to improved decision-making. This study provides an overview of the general pipeline for multimodal data integration techniques. We start by examining different types of biomedical data and exploring feature extraction and preprocessing techniques for these modalities. Moreover, we discuss data-level, feature-level, and decision-level fusion methods in detail, specifying scenarios where each fusion approach is most effective. The review also addresses the use cases and existing applications of multimodal data fusion, highlighting their impact on disease diagnosis, prognosis, treatment planning, and personalized medicine. [ABSTRACT FROM AUTHOR]

Background: Hypertension management is challenged by the dynamic nature of blood pressure (BP) fluctuations. These fluctuations are influenced by factors such as physical activity, emotional changes, and sleep patterns. Traditional guidelines focus on static measurements taken in clinical settings. However, these readings may not accurately represent an individual's typical BP levels. This approach can lead to misdiagnoses, such as white coat or masked hypertension, resulting in either unnecessary treatment or missed diagnoses. Current Concepts: Recent global hypertension guidelines emphasize the importance of home BP monitoring and 24-hour ambulatory BP measurements for more accurate diagnoses and treatment plans. However, several barriers exist, such as the high cost of ambulatory BP devices and practical challenges associated with home BP monitoring, which limit their widespread utilization. Despite these challenges, integrating BP measurements into mobile devices offers a promising approach to monitoring BP outside the clinical setting. Various innovative approaches have been developed, including smartwatches utilizing photoplethysmography sensors and machine learning algorithms. However, their accuracy and the need for periodic calibration are areas of concern. Discussion and Conclusion: Mobile device BP monitoring represents a significant advancement in hypertension management, offering the potential for continuous, convenient, and less intrusive monitoring. Despite these potential benefits, challenges such as device accuracy verification, large-scale data interpretation, and calibration must be addressed. The future of hypertension management will likely rely on these technologies, along with traditional methods, to provide better insight into individual BP profiles. This integration promises to improve hypertension diagnosis, management, and ultimately patient outcomes, although further research and development are necessary to overcome the current limitations. [ABSTRACT FROM AUTHOR]

Objectives: Pulse is a method of Korean medicine diagnosis and is an important clue to detect the organs, nature, and progress of the disease. Pulse examination is included in the basic examination of Korean medicine doctors, but there is no standardized method for diagnosing pulse although the types and methods of the pulse taking are briefly described in the literature, making it difficult to spread the examination method. In this regard, I would like to propose an objective evaluation method.Methods: Although the importance of pulse examination and the method of pulse examination are known in the literature, it is difficult for undergraduate students or inexperienced Korean medicine doctors to access it, so in this paper a method of marking the size of the pulse power in the blank space for objective evaluation was devised and presented.Results: The size of the pulse power should be indicated using the 1-cell, 3-cell, or 5-cell method according to the left and right wrists and the cun, guan and chi on both sides.Conclusion: The method of pulse diagnosis is an important diagnostic method as a verification process for making a Korean medical diagnosis. The remaining Korean medicine diagnostic methods, including pulse diagnosis, also need to undergo objectification. It is believed that the objectification of these diagnostic methods will lead to an improvement in the treatment rate of Korean medicine.