Heart Disease Prognosis Using Artificial Intelligence

Abstract

Machine learning tools are providing successful results in disease diagnosis. In the diagnosis of heart disease, the Machine Learning Techniques has been used to show the acceptable levels of accuracy. Human heartbeat has been asserted to provide promising markers of CHF. For diagnosing heart disease, it can provide solution to complex queries and thus assist healthcare practitioners to make intelligent clinical decisions which traditional decision support systems cannot. By providing this treatment, it also helps to reduce the treatment costs. To predict the heart disease of a person, the CNN is used, which is one of the classification technique of deep learning. In the existing system, they have used random forest algorithm which is one the technique of machine learning. It could provide the accuracy up-to 80%. But by using this conventional neural network, the accuracy level could be more than 90% (i.e.) the efficiency level is increased. Each person has different level of Cholesterol, Blood pressure, FBS, Resting Electrocardiogram, Pulse rate in their body. we can predict the heart disease, by using the medical terms such as blood pressure, type of chest pain, blood sugar, cholesterol. Rather than using machine learning, Deep learning algorithm will provide the result accurately.