Title: A Deep Learning for Cardiovascular Diseases Detection on Wearable Devices Data
Abstract: This research paper explores the potential of deep learning techniques in the early detection of cardiovascular diseases using data collected from wearable devices. With the advent of advanced wearable technology, continuous monitoring of physiological parameters, such as heart rate, electrocardiogram (ECG) signals, and physical activity patterns, has become feasible. Leveraging the power of deep neural networks, we investigate the development of predictive models capable of identifying individuals at risk of cardiovascular diseases. The study employs rigorous data preprocessing techniques, encompassing feature extraction and data segmentation, in conjunction with Machine Learning algorithms to enhance model accuracy. Additionally, regularization methods, notably dropout, are harnessed to address challenges posed by diverse datasets. The research envisions the incorporation of data derived from everyday activities to comprehensively assess their impact on heart health, ultimately aiming to proactively alert individuals to deteriorating cardiovascular conditions. The findings presented herein encourage further exploration and innovation in the realm of cardiovascular disease detection, fostering advancements that can enhance public health and well-being.
Keywords: deep learning;cardiovascular disease;detection;classification;wereable devices
Review score: Moderate
Review comment: The article presents a deep learning approach to detecting cardiovascular diseases (CVD) using data from wearable devices. It effectively marries the fields of biomedical engineering and information technology by applying advanced machine learning algorithms to process data obtained from wearable sensors. This is in line with the engineering direction and IT technology usage required by the IEEE for conference submissions.
The paper describes an experiment involving data collection from wearable devices and the application of deep learning models for CVD detection. The methodology includes data preprocessing, feature extraction, and the use of convolutional neural networks (CNN) and recurrent neural networks (RNN) for analysis. While the experiment's structure is outlined, the presentation could benefit from more detailed descriptions of the input and output parameters, specific diagrams illustrating the workflow, and real data results to substantiate the findings.
The conclusions are well-supported by the described methods and anticipated results, although more detailed data would enhance the argument. The paper contains figures that illustrate the methodologies and concepts but could benefit from more detailed tables and charts to represent the data and results clearly. 
Conclusion and Recommendation:
The paper offers a valuable contribution to the field of health informatics by applying deep learning techniques to wearable device data for CVD detection. It aligns well with the conference's themes and demonstrates a strong methodological approach.

Recommendation: "Accepted with minor revision" - The paper should be accepted for publication after minor revisions to enhance the clarity of the experimental data and results. This includes providing more detailed descriptions of the experimental setup, adding comprehensive diagrams and real data results, and expanding on the discussion of the findings in the context of existing literature.