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Published on: September 22, 2023
An efficient cardio vascular disease prediction using multi-scale weighted feature fusion-based convolutional neural
K Gunasekaran1, V D Ambeth Kumar2, K Jayashree3
1Department of CSE, Panimalar Engineering College, Chennai, India.
Insights
This study introduces an advanced deep learning framework for predicting cardiovascular disease (CVD), aiming to reduce global mortality rates. The novel model achieves high accuracy, offering a faster and more precise diagnostic tool.
Area of Science:
- Biomedical Engineering
- Artificial Intelligence in Healthcare
Background:
- Cardiovascular disease (CVD) poses a significant global health threat, with prediction challenges in underdeveloped regions.
- Accurate and timely diagnosis of CVD is critical for patient survival and effective treatment.
Purpose of the Study:
- To develop and evaluate an advanced deep model-based framework for cardiovascular disease prediction and risk analysis.
- To minimize global CVD-related mortality rates through improved diagnostic capabilities.
Main Methods:
- Data preprocessing involved cleaning, scaling, and handling missing values from online sources.
- Feature extraction included deep features, Principal Component Analysis (PCA), and Support Vector Machine (SVM) methods.
- A Multi-scale Weighted Feature Fusion-based Deep Structure Network (MWFF-DSN), integrating CNN and GRU, was developed for CVD prediction, optimized by a Modernized Plum Tree Algorithm (MPTA).
Main Results:
- The developed MWFF-DSN model achieved a prediction accuracy of 96%.
- The model demonstrated a high specificity of 95.95%.
- The framework offers rapid CVD detection with highly accurate results.
Conclusions:
- The proposed deep model framework provides an effective solution for cardiovascular disease prediction and risk assessment.
- This advanced approach has the potential to significantly reduce CVD mortality by enabling faster, more accurate diagnoses.
Abstract:
The cardiovascular disease (CVD) is the dangerous disease in the world. Most of the people around the world are affected by this dangerous CVD. In under-developed countries, the prediction of CVD remains the toughest job and it takes more time and cost. Diagnosing this illness is an intricate task that has to be performed precisely to save the life span of the human. In this research, an advanced deep model-based CVD prediction and risk analysis framework is proposed to minimize the death rate of humans all around the world. The data required for the prediction of CVD is collected from online data sources. Then, the input data is preprocessed using data cleaning, data scaling, and Nan and null value removal techniques. From the preprocessed data, three sets of features are extracted. The three sets of features include deep features, Principal Component Analysis (PCA), and Support Vector Machine (SVM)-based features. A Multi-scale Weighted Feature Fusion-based Deep Structure Network (MWFF-DSN) is developed to predict CVD. This structure is composed of a Multi-scale weighted Feature fusion-based Convolutional Neural Network (CNN) with a Residual Gated Recurrent Unit (GRU). The retrieved features are given as input to MWFF-DSN, and for optimizing weights, a Modernized Plum Tree Algorithm (MPTA) is developed. From the overall analysis, the developed model has attained an accuracy of 96% and it achieves a specificity of 95.95%. The developed model takes minimum time for the CVD and it gives highly accurate detection results.
