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.