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Coronary heart disease classification using deep learning approach with feature selection for improved accuracy
Anandhavalli Muniasamy1, Arshiya Begum1, Asfia Sabahath1
1College of Computer Science, King Khalid University, Abha, Saudi Arabia.
Insights
This study developed a deep learning model using Convolutional Neural Networks (CNN) and LASSO feature selection for accurate Coronary Heart Disease (CHD) prediction. The model achieved 99.36% accuracy, significantly outperforming previous methods for cardiovascular disease risk assessment.
Area of Science:
- Cardiology
- Artificial Intelligence
- Biomedical Informatics
Background:
- Coronary heart disease (CHD) is a leading cause of mortality, necessitating robust risk prediction models.
- Evaluating numerous risk factors for CHD is challenging for human experts, highlighting the need for effective feature selection.
- Deep learning (DL) models show promise for early CHD detection and cardiovascular risk assessment.
Purpose of the Study:
- To develop a deep Convolutional Neural Network (CNN) model for CHD classification.
- To employ the LASSO (least absolute shrinkage and selection operator) technique for efficient feature selection.
- To compare the proposed model's performance against existing studies using accuracy metrics.
Main Methods:
- Utilized the National Health and Nutritional Examination Survey (NHANES) CHD dataset with 49 features.
- Applied the LASSO technique for feature selection.
- Developed an improved CNN model with a feature extractor (two 1D convolution layers) and a classifier (two fully connected layers with SoftMax).
- Evaluated the model using accuracy, recall, specificity, and ROC metrics.
Main Results:
- The LASSO model effectively selected relevant features from the CHD dataset.
- The proposed CNN model achieved a high accuracy of 99.36%.
- This accuracy significantly surpasses the performance of previous models, which ranged from 80% to 92%.
Conclusions:
- The combined CNN and LASSO model offers an effective approach for CHD classification.
- This method can accelerate CHD diagnosis and improve cardiovascular disease prediction based on risk features.
- The model demonstrates significant potential for clinical application in identifying individuals at risk for cardiovascular events.
Background:
Coronary heart disease (CHD) is one of the deadliest diseases and a risk prediction model for cardiovascular conditions is needed. Due to the huge number of features that lead to heart problems, it is often difficult for an expert to evaluate these huge features into account. So, there is a need of appropriate feature selection for the given CHD dataset. For early CHD detection, deep learning modes (DL) show promising results in the existing studies.
Objective:
This study aimed to develop a deep convolution neural network (CNN) model for classification with a selected number of efficient features using the LASSO (least absolute shrinkage and selection operator) technique. Also, aims to compare the model with similar studies and analyze the performance of the proposed model using accuracy measures.
Methods:
The CHD dataset of NHANES (National Health and Nutritional Examination Survey) was examined with 49 features using LASSO technique. This research work is an attempt to apply an improved CNN model for the classification of the CHD dataset with huge features CNN model with feature extractor consists of a fully connected layer with two convolution 1D layers, and classifier part consists of two fully connected layers with SoftMax function was trained on this dataset. Metrics like accuracy recall, specificity, and ROC were used for the evaluation of the proposed model.
Results:
The feature selection was performed by applying the LASSO model. The proposed CNN model achieved 99.36% accuracy, while previous studies model achieved over 80 to 92% accuracy.
Conclusion:
The application of the proposed CNN with the LASSO model for the classification of CHD can speed up the diagnosis of CHD and appears to be effective in predicting cardiovascular disease based on risk features.
