A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion
By Farman Ali, Shaker El-Sappagh, S. M. Riazul Islam, Daehan Kwak, Amjad Ali, Muhammad Imran, K. Kwak. Information Fusion, 2020
693 citations recorded by the source corpus.
Abstract The accurate prediction of heart disease is essential to efficiently treating cardiac patients before a heart attack occurs. This goal can be achieved using an optimal machine learning model with rich healthcare data on heart diseases. Various systems based on machine learning have been presented recently to predict and diagnose heart disease. However, these systems cannot handle high-dimensional datasets due to the lack of a smart framework that can use different sources of data for heart disease prediction. In addition, the existing systems utilize conventional techniques to select features from a dataset and compute a general weight for them based on their significance. These methods have also failed to enhance the performance of heart disease diagnosis. In this paper, a smart healthcare system is proposed for heart disease prediction using ensemble deep learning and feature fusion approaches. First, the feature fusion method combines the extracted features from both sensor data and electronic medical records to generate valuable healthcare data. Second, the information gain technique eliminates irrelevant and redundant features, and selects the important ones, which decreases the computational burden and enhances the system performance. In addition, the conditional probability approach computes a specific feature weight for each class, which further improves system performance. Finally, the ensemble deep learning model is trained for heart disease prediction. The proposed system is evaluated with heart disease data and compared with traditional classifiers based on feature fusion, feature selection, and weighting techniques. The proposed system obtains accuracy of 98.5%, which is higher than existing systems. This result shows that our system is more effective for the prediction of heart disease, in comparison to other state-of-the-art methods.
Subfields: Cardiology and Cardiovascular Medicine, Computer Networks and Communications, Health Information Management
View Paper: https://www.semanticscholar.org/paper/662c34a49b79f0f336be45fec4bd0613fc9c10f3
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In Computer Networks and Communications
Citations in the corpus
- Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions (2022, background): “services [43], [44], which can be found in connected wearable devices, such as smartwatch and specialized shimmer ECG”
- A comprehensive review on ensemble deep learning: Opportunities and challenges (2023, background): “In particular, in medical image classification, the authors of Ali et al. (2020) applied a smart healthcare system for heart disease prediction using ensemble deep learning and feature fusion approaches.”
- The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century (2024, background): “Another interesting study introduces a cutting-edge healthcare system that employs ensemble deep learning coupled with feature fusion approaches [39].”
- Ensemble deep learning: A review (2021, background): “2020 A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion[222] Boosting Heart disease prediction”