Heart Disease Detection with Deep Learning Using a Combination of Multiple Input Sources

Abstract

Disease detection is quite a difficult task and it requires medical training and experience. When it comes to heart disease diagnostics, it gets even more complex due to the additional specialization and expertise required. One of the common ways for detecting heart diseases used by heart specialists is by listening to the patient's heartbeat via a stethoscope and interpreting the sound. In this paper, we describe how we can use deep learning techniques to detect heart diseases in patients by analyzing audio recording or real time streaming of heartbeats to predict whether the patient is healthy or sick. The deep neural network model developed is a hybrid model as it combines the innate characteristics of convolutional and recurrent neural network models, which helped achieve higher accuracy in the prediction results as compared to current published results. The dataset used for training the model is a combination of two separate data science competition datasets. We used data augmentation techniques on this combined dataset to increase its size and diversity. When we tested the final model, it showed a significantly better performance as compared to the state of the art.