Novel Deep Learning Architecture for Heart Disease Prediction using Convolutional Neural Network

TL;DR

This paper introduces a novel 1D convolutional neural network architecture for early heart disease detection, achieving high accuracy and addressing limitations of classical models in generalization.

Contribution

The paper presents a new deep learning model that improves generalization in heart disease classification using clinical data and techniques to prevent overfitting.

Findings

Achieved over 97% training accuracy

Achieved 96% test accuracy

Outperformed other classification algorithms

Abstract

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.