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Hybrid optimized temporal convolutional networks with long short-term memory for heart disease prediction with deep
1Research Scholar, Department of Computer Science and Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, India.
Insights
This study introduces a novel hybrid deep learning model for early heart disease prediction. The advanced system achieves high accuracy, improving patient outcomes and aiding medical professionals in timely diagnosis.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Informatics
Background:
- Heart disease is a leading global cause of mortality.
- Early detection of heart conditions remains a significant challenge in healthcare.
- Accurate prediction models are crucial for timely intervention and improved patient survival rates.
Purpose of the Study:
- To develop and implement a novel heart disease prediction model using a hybrid deep learning strategy.
- To enhance the accuracy and efficiency of early heart disease detection.
- To provide a robust tool for medical professionals to aid in patient diagnosis.
Main Methods:
- A hybrid deep learning framework combining One-Dimensional Convolutional Neural Network (1DCNN) for feature extraction.
- Integration of Temporal Convolutional Networks (TCN) with Long Short-Term Memory (LSTM) for classification.
- Optimization of model parameters using the Enhanced Forensic-Based Investigation (EFBI) meta-optimization algorithm.
Main Results:
- The proposed model achieved a high accuracy rate of 98.67%.
- The precision rate of the developed heart disease prediction system was 99.48%.
- The system demonstrated superior performance compared to existing methods in evaluation metrics.
Conclusions:
- The novel hybrid deep learning approach offers a promising solution for accurate and early heart disease prediction.
- The developed model can significantly aid in the early diagnosis and management of heart conditions.
- This research contributes to advancing AI applications in cardiovascular disease detection.
Abstract:
A heart attack is intended as top prevalent among all ruinous ailments. Day by day, the number of affected people count is increasing globally. The medical field is struggling to detect heart disease in the initial step. Early prediction can help patients to save their life. Thus, this paper implements a novel heart disease prediction model with the help of a hybrid deep learning strategy. The developed framework consists of various steps like (i) Data collection, (ii) Deep feature extraction, and (iii) Disease prediction. Initially, the standard medical data from various patients are acquired from the clinical standard datasets. Here, a One-Dimensional Convolutional Neural Network (1DCNN) is utilized for extracting the deep features from the acquired medical data to minimize the number of redundant data from the gathered large-scale data. The acquired deep features are directly fed to the Hybrid Optimized Deep Classifier (HODC) with the integration of Temporal Convolutional Networks (TCN) with Long Short-Term Memory (LSTM), where the parameters in both classifiers are optimized using the newly suggested Enhanced Forensic-Based Investigation (EFBI) inspired meta-optimization algorithm. Throughout the result analysis, the accuracy and precision rate of the offered approach is 98.67% and 99.48%. The evaluation outcomes show that the recommended system outperforms the extant systems in terms of performance metrics examination.
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