Arailym-tleubayeva/sist-english-corpus huggingface dataset:
Title: A Machine Learning Approach for Detection of Heart Diseases Using Wearable Devices
Abstract: This research paper delves into the integration of wearable devices and deep learning techniques to enhance heart disease detection and monitoring. The study highlights the importance of continuous cardiac health assessment and the transformative potential of wearable technology in the healthcare landscape. Our investigation showcases their efficacy in processing intricate physiological data for early heart disease recognition. Key variables such as age, maximum heart rate, blood pressure, cholesterol levels, and various electrocardiography (ECG) metrics are explored as pivotal factors in predicting and comprehending heart disease risk factors. Furthermore, we emphasize the significance of data normalization methodologies and secure data storage solutions to ensure data reliability and accessibility. Correlation analyses elucidate the intricate interplay among these variables, providing valuable insights into their collective influence on cardiac health. As the realm of machine learning and healthcare technology continues to evolve, this research lays a solid foundation for advancing heart disease detection and management, ultimately aiming to enhance patient outcomes and elevate the quality of healthcare delivery.
Keywords: artificial intelligence;machine learning;classification;detection;heart disease;wereable devices
Review score: High
Review comment: This research paper delves into the integration of wearable devices and deep learning techniques to enhance heart disease detection and monitoring. The topic of the article is relevant. The goal is to develop an accurate and reliable deep learning-based system to continuously monitor people's heart health through devices, identify potential heart abnormalities or diseases, and promptly alert healthcare professionals or individuals.The approach the authors propose involves carefully examining sensor data to create a comprehensive behavior set.
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: High
Review comment: The article is devoted to the problem of deep learning for identifying cardiovascular diseases using data from wearable devices.
It is well structured, including an introduction, a summary of the main idea, an experiment section, and conclusions.

However, it has some shortcomings that need to be fixed:
1. The section “Analysis of recent research and publications” needs to be expanded and supplemented.
2. it is necessary to clearly formulate the scientific novelty of the research, its tasks and goals. conclusions must be related to objectives.
3. The section describing the main materials of the study is not sufficiently substantiated, the presented results are not confirmed.
4. It is necessary to improve the quality of rice 1 and 2. 

The article may be accepted for publication after correcting the remarks and re-reviewing