A Robust Framework for Data Generative and Heart Disease Prediction Based on Efficient Deep Learning Models

Raniya R Sarra; Ahmed M Dinar; Mazin Abed Mohammed; Mohd Khanapi Abd Ghani; Marwan Ali Albahar

doi:10.3390/diagnostics12122899

A Robust Framework for Data Generative and Heart Disease Prediction Based on Efficient Deep Learning Models

Diagnostics (Basel). 2022 Nov 22;12(12):2899. doi: 10.3390/diagnostics12122899.

Authors

Raniya R Sarra¹, Ahmed M Dinar¹, Mazin Abed Mohammed², Mohd Khanapi Abd Ghani³, Marwan Ali Albahar⁴

Affiliations

¹ Computer Engineering Department, University of Technology, Baghdad 00964, Iraq.
² College of Computer Science and Information Technology, University of Anbar, Ramadi 31001, Iraq.
³ Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal 76100, Malaysia.
⁴ Department of Computer Science, Umm Al Qura University, Mecca 24211, Saudi Arabia.

Abstract

Biomarkers including fasting blood sugar, heart rate, electrocardiogram (ECG), blood pressure, etc. are essential in the heart disease (HD) diagnosing. Using wearable sensors, these measures are collected and applied as inputs to a deep learning (DL) model for HD diagnosis. However, it is observed that model accuracy weakens when the data gathered are scarce or imbalanced. Therefore, this work proposes two DL-based frameworks, GAN-1D-CNN, and GAN-Bi-LSTM. These frameworks contain: (1) a generative adversarial network (GAN) and (2) a one-dimensional convolutional neural network (1D-CNN) or bi-directional long short-term memory (Bi-LSTM). The GAN model is utilized to augment the small and imbalanced dataset, which is the Cleveland dataset. The 1D-CNN and Bi-LSTM models are then trained using the enlarged dataset to diagnose HD. Unlike previous works, the proposed frameworks increase the dataset first to avoid the prediction bias caused by the limited data. The GAN-1D-CNN achieved 99.1% accuracy, specificity, sensitivity, F1-score, and 100% area under the curve (AUC). Similarly, the GAN-Bi-LSTM obtained 99.3% accuracy, 99.2% specificity, 99.3% sensitivity, 99.2% F1-score, and 100% AUC. Furthermore, time complexity of proposed frameworks is investigated with and without principal component analysis (PCA). The PCA method reduced prediction times for 61 samples using GAN-1D-CNN and GAN-Bi-LSTM to 68.8 and 74.8 ms, respectively. These results show that it is reliable to use our frameworks for augmenting limited data and predicting heart disease.

Keywords: artificial intelligence; bi-directional long short-term memory; data augmentation; deep learning; generative adversarial network; heart disease prediction; one-dimensional convolutional neural network.

Grants and funding

The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: 22UQU4400257DSR08.