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Early Detection & Prediction of Heart Disease using Various Machine Learning Approaches

It's no secret that heart disease is a significant source of public health concern. Heart patients are on the rise because of a lack of health awareness and unhealthy eating habits. Because of this, it is vital to establish a basis that can quickly identify the occurrence of cardiac disease in hundreds of samples. This paper tested these approaches for their ability to predict cardiac disease at this point. Based on six different categorization performance indices, evaluated these approaches. In addition, using the receiver operating characteristic curve was used to evaluate these techniques. The Neural network classification algorithm was found to have a maximum classification accuracy of 98.3%.