Development of an efficient novel method for coronary artery disease prediction using machine learning and deep
C M M Mansoor1,2, Sarat Kumar Chettri3, H M M Naleer4
1Assam Don Bosco University, Guwahati, India.
Summary
This study introduces a novel machine learning model for early coronary artery disease (CAD) detection, achieving 92% accuracy. The advanced deep learning approach significantly improves prediction compared to existing methods.
Area of Science:
- Cardiovascular medicine
- Medical informatics
- Machine learning
Background:
- Heart disease, particularly coronary artery disease (CAD), is a leading cause of global mortality.
- Early detection of cardiovascular conditions like CAD is crucial for improving patient outcomes and reducing fatality rates.
- Machine learning (ML) is increasingly utilized in healthcare for the analysis of clinical data to aid in disease diagnosis.
Purpose of the Study:
- To develop and evaluate a novel deep learning (DL) algorithm for enhanced prediction accuracy of coronary artery disease (CAD).
- To compare the performance of the proposed DL model against established ML algorithms in a classification context.
Main Methods:
- An ensemble voting classifier was constructed using multiple ML algorithms including Naïve Bayes, Logistic Regression, Decision Tree, XGBoost, Random Forest, CNN, SVM, KNN, Bidirectional LSTM, and LSTM.
- The Alizadeh Sani dataset, comprising 216 CAD cases, was utilized. Feature selection was optimized using the Chi-square test, and the Synthetic Minority Over Sampling Technique (SMOTE) was applied to handle data imbalance.
- Performance evaluation employed metrics such as accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve (AUC-ROC), and confusion matrix analysis.
Main Results:
- The novel DL model demonstrated superior performance in CAD prediction.
- The proposed model achieved a high prediction accuracy rate of 92% for detecting coronary artery disease.
- The results indicate that the novel algorithm is competitive with and comparable to state-of-the-art methods.
Conclusions:
- The novel machine learning model significantly enhances the accuracy of coronary artery disease detection.
- The developed deep learning approach offers a robust and effective solution for early CAD identification.
- This study highlights the potential of advanced ML techniques in improving cardiovascular disease diagnosis and patient care.
