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.
Insights
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.
Background:
Heart disease is a severe health issue that results in high fatality rates worldwide. Identifying cardiovascular diseases such as coronary artery disease (CAD) and heart attacks through repetitive clinical data analysis is a significant task. Detecting heart disease in its early stages can save lives. The most lethal cardiovascular condition is CAD, which develops over time due to plaque buildup in coronary arteries, causing incomplete blood flow obstruction. Machine Learning (ML) is progressively used in the medical sector to detect CAD disease.
Objective:
The primary aim of this work is to deliver a state-of-the-art approach to enhancing CAD prediction accuracy by using a DL algorithm in a classification context.
Methods:
A unique ML technique is proposed in this study to predict CAD disease accurately using a deep learning algorithm in a classification context. An ensemble voting classifier classification model is developed based on various methods such as Naïve Bayes (NB), Logistic Regression (LR), Decision Tree (DT), XGBoost, Random Forest (RF), Convolutional Neural Network (CNN), Support Vector Machine (SVM), K Nearest Neighbor (KNN), Bidirectional LSTM and Long Short-Term Memory (LSTM). The performance of the ensemble models and a novel model are compared in this study. The Alizadeh Sani dataset, which consists of a random sample of 216 cases with CAD, is used in this study. Synthetic Minority Over Sampling Technique (SMOTE) is used to address the issue of imbalanced datasets, and the Chi-square test is used for feature selection optimization. Performance is assessed using various assessment methodologies, such as confusion matrix, accuracy, recall, precision, f1-score, and auc-roc.
Results:
When a novel algorithm achieves the highest accuracy relative to other algorithms, it demonstrates its effectiveness in several ways, including superior performance, robustness, generalization capability, efficiency, innovative approaches, and benchmarking against baselines. These characteristics collectively contribute to establishing the novel algorithm as a promising solution for addressing the target problem in machine learning and related fields.
Conclusion:
Implementing the novel model in this study significantly improved performance, achieving a prediction accuracy rate of 92% in the detection of CAD. These findings are competitive and on par with the top outcomes among other methods.
