Heart disease refers to a range of conditions that affect the heart, such as coronary heart disease, arrhythmias, and heart failure. According to the World Health Organization (WHO), heart disease is the number 1 killer globally. Despite the improvements achieved in the healthcare systems, it remains the disease that contributes the most increasing burden to the older population. Furthermore, heart disease patients do not only need long-term care and monitoring but are also forced to pay higher medical costs and may suffer from mental health problems. However, many forms of heart disease can be prevented or improved by adopting healthy lifestyle choices, such as adhering to a balanced diet, exercise regularly, and have enough good-quality sleep.
In this project, a web-based system is built to keep track of the data and analytics results of heart disease patients. Habit data of patients such as diet, exercise, and sleep are collected using smartwatches and Samsung Health App. Then, big data analytics and machine learning techniques are applied for classifying habit-change of heart disease patients into five levels, which are no change, little change, medium change, high change, and very high change. Besides, their electrocardiogram (ECG) and heartbeat sound of these patients are also collected. ECG signals are utilized to classify whether a patient is stressed or not. On the other hand, heartbeat sounds are classified into three classes, including normal, murmur, and extrasystole. After that, all analytics results are visualized and presented to doctors through the intelligent digital dashboard. It can be a significant tool to assist doctors in making medical decisions effectively.