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Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm
Zeinab Arabasadi1, Roohallah Alizadehsani2, Mohamad Roshanzamir3
1Department of Computer Engineering, University of Bojnord, Bojnord, Iran.
Insights
This study introduces a hybrid machine learning model for diagnosing coronary artery disease (CAD). The novel approach enhances neural network performance, offering a cost-effective and accurate alternative to traditional methods.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Medicine
- Cardiology
Background:
- Cardiovascular disease, particularly coronary artery disease (CAD), is a leading global cause of mortality.
- Traditional diagnostic methods like angiography are effective but costly and carry risks.
- Machine learning and data mining offer promising avenues for developing alternative diagnostic tools.
Purpose of the Study:
- To propose a highly accurate hybrid method for the diagnosis of coronary artery disease.
- To improve the performance of neural networks in CAD diagnosis through weight enhancement.
Main Methods:
- Development of a hybrid model combining genetic algorithms and neural networks.
- Utilizing genetic algorithms to optimize initial neural network weights for enhanced performance.
- Evaluation of the model on the Z-Alizadeh Sani dataset.
Main Results:
- Achieved a diagnostic accuracy of 93.85%.
- Demonstrated high sensitivity (97%) and specificity (92%) in identifying coronary artery disease.
- The hybrid approach improved neural network performance by approximately 10%.
Conclusions:
- The proposed hybrid method provides a highly accurate and potentially more accessible approach for coronary artery disease diagnosis.
- Optimizing neural network weights with genetic algorithms significantly enhances diagnostic capabilities.
- This AI-driven methodology presents a viable alternative to conventional diagnostic techniques.
Abstract:
Cardiovascular disease is one of the most rampant causes of death around the world and was deemed as a major illness in Middle and Old ages. Coronary artery disease, in particular, is a widespread cardiovascular malady entailing high mortality rates. Angiography is, more often than not, regarded as the best method for the diagnosis of coronary artery disease; on the other hand, it is associated with high costs and major side effects. Much research has, therefore, been conducted using machine learning and data mining so as to seek alternative modalities. Accordingly, we herein propose a highly accurate hybrid method for the diagnosis of coronary artery disease. As a matter of fact, the proposed method is able to increase the performance of neural network by approximately 10% through enhancing its initial weights using genetic algorithm which suggests better weights for neural network. Making use of such methodology, we achieved accuracy, sensitivity and specificity rates of 93.85%, 97% and 92% respectively, on Z-Alizadeh Sani dataset.
