  Healthcare is one of the most important aspects of human life. Heart disease
is known to be one of the deadliest diseases which is hampering the lives of
many people around the world. Heart disease must be detected early so the loss
of lives can be prevented. The availability of large-scale data for medical
diagnosis has helped developed complex machine learning and deep learning-based
models for automated early diagnosis of heart diseases. The classical
approaches have been limited in terms of not generalizing well to new data
which have not been seen in the training set. This is indicated by a large gap
in training and test accuracies. This paper proposes a novel deep learning
architecture using a 1D convolutional neural network for classification between
healthy and non-healthy persons to overcome the limitations of classical
approaches. Various clinical parameters are used for assessing the risk profile
in the patients which helps in early diagnosis. Various techniques are used to
avoid overfitting in the proposed network. The proposed network achieves over
97% training accuracy and 96% test accuracy on the dataset. The accuracy of the
model is compared in detail with other classification algorithms using various
performance parameters which proves the effectiveness of the proposed
architecture.
