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Early heart disease prediction using LV-PSO and Fuzzy Inference Xception Convolution Neural Network on
1Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, India.
Insights
This study introduces a new method for early heart disease prediction using phonocardiogram (PCG) signals. The advanced system achieves high accuracy in classifying heart conditions, improving diagnostic capabilities.
Area of Science:
- Cardiology
- Artificial Intelligence
- Signal Processing
Background:
- Heart disease is a leading global cause of mortality, necessitating early detection for effective treatment.
- Phonocardiogram (PCG) signal analysis shows promise for diagnosing cardiovascular conditions, but high dimensionality poses classification challenges.
- Conventional PCG analysis systems often suffer from misclassification and reduced performance due to complex features.
Purpose of the Study:
- To develop an advanced system for early heart risk prediction using phonocardiogram (PCG) signals.
- To overcome the limitations of conventional PCG analysis, including high dimensionality and misclassification.
- To enhance the accuracy and reliability of cardiovascular condition diagnosis through innovative computational methods.
Main Methods:
- Proposed a novel framework integrating Linear Vectored Particle Swarm Optimization (LV-PSO) with a Fuzzy Inference Xception Convolutional Neural Network (XCNN).
- Extracted key variations from PCG signals, including delta, theta, diastolic, and systolic differences.
- Employed a Support Scalar Cardiac Impact Rate (S2CIR) for disease-specific scalar variation analysis and utilized LV-PSO for feature dimensionality reduction before XCNN training.
Main Results:
- The proposed LV-PSO integrated with Fuzzy Inference XCNN demonstrated superior predictive performance over existing models.
- Achieved a precision of 95.6%, a recall of 93.1%, and an overall prediction accuracy of 95.8% across various heart disease categories.
- The system effectively enhanced feature selection and classification accuracy for PCG-based diagnostics.
Conclusions:
- The integration of LV-PSO and Fuzzy Inference XCNN significantly improves the diagnostic capabilities of PCG-based systems.
- The proposed framework offers a reliable tool for early heart disease prediction.
- This approach holds substantial potential for clinical decision support in cardiovascular healthcare.
Introduction:
Heart disease is one of the leading causes of mortality worldwide, and early detection is crucial for effective treatment. Phonocardiogram (PCG) signals have shown potential in diagnosing cardiovascular conditions. However, accurate classification of PCG signals remains challenging due to high dimensional features, leading to misclassification and reduced performance in conventional systems.
Methods:
To address these challenges, we propose a Linear Vectored Particle Swarm Optimization (LV-PSO) integrated with a Fuzzy Inference Xception Convolutional Neural Network (XCNN) for early heart risk prediction. PC G signals are analyzed to extract variations such as delta, theta, diastolic, and systolic differences. A Support Scalar Cardiac Impact Rate (S2CIR) is employed to capture disease specific scalar variations and behavioral impacts. LV-PSO is used to reduce feature dimensionality, and the optimized features are subsequently trained using the Fuzzy Inference XCNN model to classify disease types.
Results:
Experimental evaluation demonstrates that the proposed system achieves superior predictive performance compared to existing models. The method attained a precision of 95.6%, recall of 93.1%, and an overall prediction accuracy of 95.8% across multiple disease categories.
Discussion:
The integration of LV-PSO with Fuzzy Inference XCNN enhances feature selection aPSO with Fuzzy Inference XCNN enhances feature selection and nd classification accuracy, significantly improving the diagnostic capabilities of PCG-classification accuracy, significantly improving the diagnostic capabilities of PCG-based systems. These results highlight the potential of the proposed framework as a based systems. These results highlight the potential of the proposed framework as a reliable tool for early heart disease prediction and clinical decision support.reliable tool for early heart disease prediction and clinical decision support.
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