Efficient Coronary Heart Disease Prediction: Enhanced Neural Network with Chaos Salp Decision and Feature Optimization


  • A. Asha Professor, Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, Chennai 602105, Tamil Nadu, India.
  • B. Dhiyanesh Associate Professor, Department of Computer Science and Engineering, Dr. N.G.P. Institute of Technology, Coimbatore 614048, Tamil Nadu, India.
  • G. Kiruthiga Professor, Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore 641032, Tamil Nadu
  • L. Shakkeera Associate Professor, Department of Computer Science and Engineering, Presidency University, Bengaluru 560064, Karnataka, India.
  • Vinodkumar Jacob Professor, Department of Electronics and Communication Engineering, M.A College of Engineering, Kothamangalam 686666, Kerala, India
  • Anita Venaik Professor, Department of Information Technology, Amity Business School, Amity University, Noida 201313, India


Classification, Decision, Heart Disease, Neural Network, Optimization


In recent times, Coronary Heart Disease (CHD), with its high incidence and mortality risk, has emerged as a significant threat to human health. HD prediction is a challenging task that requires expertise and advanced learning, so physicians cannot predict it efficiently. Early HD diagnosis through clinical examination and simple physical indicators is necessary. Medical professionals face challenges in diagnosing HD. Nevertheless, comparing techniques to determine which one is faster or more accurate is a time-consuming and challenging task. However, since CHD HD is not a benign disease, finding and analysing CHD markers in screening is difficult. To solve this problem, we propose the Enhanced Elman Selfish Optimization Neural Network (EESONN) to find CHD prediction by selecting the best features based on the Chaos Salp Group Decision Function (CSSDF) method. Additionally, we collected data from Kaggle using the Cleveland Clinic Cardiology Dataset. We also process the data before Improved Quality Feature Group (IQFG), such as lack of significance, normalization and standardization. In addition, the Weighted Relief Algorithm (WRA) can be applied to eliminate redundant features and determine each feature’s weight. Furthermore, optimal features can be selected using the CSSDF algorithm in the feature selection process. Finally, the proposed EESONN method to detect predictive data can effectively predict CHD classification. Furthermore, the proposed EESONN method enhances classifier prediction accuracy and performs well in HD detection. Additionally, the EESONN method enables the calculation of sensitivity, precision, specificity, accuracy, and F-score. This can be used to determine the accuracy and probability of the results.


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How to Cite

Asha, A. ., Dhiyanesh, B. ., Kiruthiga, G. ., Shakkeera, L. ., Jacob, V. ., & Venaik, A. . (2024). Efficient Coronary Heart Disease Prediction: Enhanced Neural Network with Chaos Salp Decision and Feature Optimization. International Journal of Intelligent Systems and Applications in Engineering, 12(20s), 440–450. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5156



Research Article