Cardiovascular diseases (CVDs) are the number one leading cause of death in the world according to the World Health Organization. In 2012, 17.5 Million deaths were caused by CVDs accounting for 31% of all global deaths. Cheap and early detection of CVDs can have allow individuals to acquire the appropriate treatment options as soon as possible. Detecting heart abnormalities can have a large impact on the global health and is hence of great value to the scientific community.
This python notebook studies a method of classifying heartbeat anomalies using an Artificial Neural Network.