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Published on: August 9, 2024
A data mining approach for diagnosis of coronary artery disease
Roohallah Alizadehsani1, Jafar Habibi, Mohammad Javad Hosseini
1Software Engineering, Department of Computer Engineering, Sharif University of Technology, Azadi Avenue, Tehran, Iran.
Insights
This study introduces a new data mining approach for diagnosing coronary artery disease (CAD), achieving 94.08% accuracy. The method enhances patient data and identifies key diagnostic features, offering a more cost-effective and less invasive alternative to angiography.
Area of Science:
- Cardiology
- Medical Informatics
- Data Mining
Background:
- Cardiovascular diseases are a leading cause of mortality worldwide.
- Accurate and timely diagnosis of coronary artery disease (CAD) is critical.
- Current diagnostic methods like angiography are invasive, costly, and have side effects.
Purpose of the Study:
- To develop a highly accurate, cost-effective, and less invasive method for CAD diagnosis.
- To introduce a new dataset (Z-Alizadeh Sani) and a feature creation technique for CAD analysis.
- To identify the most effective features for CAD prediction using data mining.
Main Methods:
- Utilized the Z-Alizadeh Sani dataset with 303 patients and 54 features.
- Developed a novel feature creation method to enrich the dataset.
- Applied Information Gain and confidence metrics to evaluate feature effectiveness.
- Employed data mining algorithms for CAD classification.
Main Results:
- Achieved a diagnostic accuracy of 94.08%, surpassing existing methods.
- Identified Typical Chest Pain, Region RWMA2, and age as highly effective features via Information Gain.
- Determined Q Wave and ST Elevation exhibited the highest confidence scores.
- Demonstrated the efficacy of the proposed feature creation algorithm.
Conclusions:
- The proposed data mining approach, combined with feature engineering, significantly improves CAD diagnosis accuracy.
- This method offers a promising alternative to traditional diagnostic techniques.
- Key clinical and electrocardiographic features are vital for accurate CAD prediction.
Abstract:
Cardiovascular diseases are very common and are one of the main reasons of death. Being among the major types of these diseases, correct and in-time diagnosis of coronary artery disease (CAD) is very important. Angiography is the most accurate CAD diagnosis method; however, it has many side effects and is costly. Existing studies have used several features in collecting data from patients, while applying different data mining algorithms to achieve methods with high accuracy and less side effects and costs. In this paper, a dataset called Z-Alizadeh Sani with 303 patients and 54 features, is introduced which utilizes several effective features. Also, a feature creation method is proposed to enrich the dataset. Then Information Gain and confidence were used to determine the effectiveness of features on CAD. Typical Chest Pain, Region RWMA2, and age were the most effective ones besides the created features by means of Information Gain. Moreover Q Wave and ST Elevation had the highest confidence. Using data mining methods and the feature creation algorithm, 94.08% accuracy is achieved, which is higher than the known approaches in the literature.
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