# Enhancing cardiac disease prediction with explainable bidirectional LSTM

## TL;DR

This paper introduces explainable bidirectional LSTM models for predicting and classifying cardiac diseases using ECG data, improving accuracy and interpretability.

## Contribution

The novel contribution is stacking bidirectional LSTM with deep learning for explainable cardiac disease prediction using SHAP explanations.

## Key findings

- The proposed models outperform existing methods in accuracy, precision, f1-score, and recall.
- SHAP is used to provide explanations for model predictions, aiding in ECG report annotation.

## Abstract

Cardiovascular disorders (heart diseases) are the most prevalent cause of death on a global scale. So early detection and classification increase the likelihood of survival. In the context of machine learning techniques, there is always a need for an accurate and explainable predictive model for detecting various diseases, such as cardiac disorders. The work carried out in this paper stacks bidirectional long short-term memory with deep learning to propose two models. The first model is used to detect cardiac disease with a binary label classification, while the second one classifies cardiac disease, which is a multi-label classification problem. Bidirectional LSTM is used as an approximate algorithm for feature extraction. Deep learning is used for classification purposes. The proposed models are trained and validated over the PTB-XL dataset. The performance of these models is evaluated and compared against state-of-the-art methods. The comparison shows the proposed model outperforms other methods in terms of accuracy, precision, f1-score, and recall. SHAP is used to make these models explainable, which in turn helps to annotate different diseases on the ECG report.