Use artificial intelligence technology to build machine learning models and analyze the results of human experience to predict the risk of heart disease. Using thousands of pieces of heart disease data from the UCI machine learning database, artificial intelligence algorithms were used to build the model, and 14 kinds of physical examination results were used as attributes to train the model and evaluate the risk of heart disease.
Heart disease is the number one killer of human beings. In 2015, 17.7 million people died of heart disease worldwide, accounting for 31% of the total deaths. If the risk of heart disease can be predicted, most heart diseases can be avoided by changing lifestyle behaviors such as quitting smoking, eating healthy, avoiding obesity, and increasing exercise.
This artificial intelligence model uses some of the heart patient data accumulated by the Cleveland Medical Foundation, the Hungarian Heart Association, the California Veterans Medical Center, and the University of Munich Medical School to conduct targeted training on specialized machine learning models to predict the risk of heart disease.