On Deep Neural Networks for Detecting Heart Disease

TL;DR

This paper develops and optimizes deep neural network architectures, especially the novel HEARO-5, for improved heart disease detection using clinical data, achieving state-of-the-art accuracy and robustness.

Contribution

Design and evaluation of a novel five-layer DNN architecture, HEARO-5, with regularization and data handling features, for accurate heart disease diagnosis.

Findings

HEARO-5 achieves 99% accuracy and 0.98 MCC.

HEARO-5 outperforms existing models in the literature.

Open source implementation facilitates further research.

Abstract

Heart disease is the leading cause of death, and experts estimate that approximately half of all heart attacks and strokes occur in people who have not been flagged as "at risk." Thus, there is an urgent need to improve the accuracy of heart disease diagnosis. To this end, we investigate the potential of using data analysis, and in particular the design and use of deep neural networks (DNNs) for detecting heart disease based on routine clinical data. Our main contribution is the design, evaluation, and optimization of DNN architectures of increasing depth for heart disease diagnosis. This work led to the discovery of a novel five layer DNN architecture - named Heart Evaluation for Algorithmic Risk-reduction and Optimization Five (HEARO-5) -- that yields best prediction accuracy. HEARO-5's design employs regularization optimization and automatically deals with missing data and/or data outliers. To evaluate and tune the architectures we use k-way cross-validation as well as Matthews correlation coefficient (MCC) to measure the quality of our classifications. The study is performed on the publicly available Cleveland dataset of medical information, and we are making our developments open source, to further facilitate openness and research on the use of DNNs in medicine. The HEARO-5 architecture, yielding 99% accuracy and 0.98 MCC, significantly outperforms currently published research in the area.