Genetic Algorithm-based Convolutional Neural Network Feature Engineering for Optimizing Coronary Heart Disease Prediction Performance
10.4258/hir.2024.30.3.234
- Author:
Erwin Yudi HIDAYAT
1
;
Yani Parti ASTUTI
;
Ika Novita DEWI
;
Abu SALAM
;
Moch. Arief SOELEMAN
;
Zainal Arifin HASIBUAN
;
Ahmed Sabeeh YOUSIF
Author Information
1. Faculty of Computer Science, Universitas Dian Nuswantoro, Semarang, Indonesia
- Publication Type:Original Article
- From:Healthcare Informatics Research
2024;30(3):234-243
- CountryRepublic of Korea
- Language:English
-
Abstract:
Objectives:This study aimed to optimize early coronary heart disease (CHD) prediction using a genetic algorithm (GA)-based convolutional neural network (CNN) feature engineering approach. We sought to overcome the limitations of traditional hyperparameter optimization techniques by leveraging a GA for superior predictive performance in CHD detection.
Methods:Utilizing a GA for hyperparameter optimization, we navigated a complex combinatorial space to identify optimal configurations for a CNN model. We also employed information gain for feature selection optimization, transforming the CHD datasets into an image-like input for the CNN architecture. The efficacy of this method was benchmarked against traditional optimization strategies.
Results:The advanced GA-based CNN model outperformed traditional methods, achieving a substantial increase in accuracy. The optimized model delivered a promising accuracy range, with a peak of 85% in hyperparameter optimization and 100% accuracy when integrated with machine learning algorithms, namely naïve Bayes, support vector machine, decision tree, logistic regression, and random forest, for both binary and multiclass CHD prediction tasks.
Conclusions:The integration of a GA into CNN feature engineering is a powerful technique for improving the accuracy of CHD predictions. This approach results in a high degree of predictive reliability and can significantly contribute to the field of AI-driven healthcare, with the possibility of clinical deployment for early CHD detection. Future work will focus on expanding the approach to encompass a wider set of CHD data and potential integration with wearable technology for continuous health monitoring.
