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AttGRU-HMSI: enhancing heart disease diagnosis using hybrid deep learning approach
G Madhukar Rao1,2, Dharavath Ramesh1,3, Vandana Sharma4
1Department of Computer Science and Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, 826004, India.
Insights
This study introduces a novel hybrid deep learning model for accurate heart disease prediction using big data analytics. The advanced approach achieved a 95.42% accuracy rate, significantly improving early detection of cardiovascular illnesses.
Area of Science:
- Cardiology
- Computer Science
- Data Science
Background:
- Heart disease is a leading cause of global mortality, necessitating improved diagnostic tools.
- Early identification of cardiovascular illnesses like heart attacks is crucial for saving lives.
- Machine learning (ML) and Big Data offer powerful capabilities for accurate disease forecasting and decision support.
Purpose of the Study:
- To develop and evaluate a hybrid deep learning approach for heart disease detection using big data.
- To enhance the accuracy and efficiency of cardiovascular illness prediction.
- To address the research gap in early heart disease identification through advanced analytics.
Main Methods:
- A hybrid deep learning algorithm integrating attention-based gated recurrent unit (AttGRU) network with bio-inspired hybrid mutation-based swarm intelligence (HMSI).
- Data preprocessing involved improved k-means clustering (IKC) for outlier removal and synthetic minority over-sampling technique (SMOTE) for class balancing.
- Recursive feature elimination (RFE) identified key features, and the model was compared against sparse autoencoder + artificial neural network (SAE+ANN), logistic regression (LR), K-nearest neighbor (KNN), and naïve Bayes.
Main Results:
- The proposed hybrid HMSI-AttGRU model achieved a high accuracy rate of 95.42% in heart disease prediction.
- This performance significantly outperformed the other compared machine learning algorithms.
- The approach demonstrated effectiveness in handling large medical datasets and identifying complex patterns.
Conclusions:
- The developed hybrid deep learning model offers a highly accurate and effective solution for heart disease prediction.
- This big data analysis and visualization approach holds significant promise for improving early detection and management of cardiovascular diseases.
- The study successfully addressed limitations in existing methods, paving the way for more robust clinical decision support systems.
Abstract:
Heart disease is a major global cause of mortality and a major public health problem for a large number of individuals. A major issue raised by regular clinical data analysis is the recognition of cardiovascular illnesses, including heart attacks and coronary artery disease, even though early identification of heart disease can save many lives. Accurate forecasting and decision assistance may be achieved in an effective manner with machine learning (ML). Big Data, or the vast amounts of data generated by the health sector, may assist models used to make diagnostic choices by revealing hidden information or intricate patterns. This paper uses a hybrid deep learning algorithm to describe a large data analysis and visualization approach for heart disease detection. The proposed approach is intended for use with big data systems, such as Apache Hadoop. An extensive medical data collection is first subjected to an improved k-means clustering (IKC) method to remove outliers, and the remaining class distribution is then balanced using the synthetic minority over-sampling technique (SMOTE). The next step is to forecast the disease using a bio-inspired hybrid mutation-based swarm intelligence (HMSI) with an attention-based gated recurrent unit network (AttGRU) model after recursive feature elimination (RFE) has determined which features are most important. In our implementation, we compare four machine learning algorithms: SAE + ANN (sparse autoencoder + artificial neural network), LR (logistic regression), KNN (K-nearest neighbour), and naïve Bayes. The experiment results indicate that a 95.42% accuracy rate for the hybrid model's suggested heart disease prediction is attained, which effectively outperforms and overcomes the prescribed research gap in mentioned related work.
