Optimizing Neural Networks Using a Group Learning Algorithm for Heart Disease Prediction with the Cleveland Dataset
Abstract
One of the major health issues that afflict human beings is heart disease which is regarded as a major cause of death in the world. Neural networks are just one of the ways used to forecast heart disease. When creating neural networks, one might employ the traditional optimization algorithms to explore more complicated search spaces and quickly locate global optima. This paper proposed an optimization strategy based on the Group Learning Algorithm (GLA) to adjust vital neural network parameters and feature selection was also implemented in the training. Cleveland dataset was used in the experiment. The strategy enhances the accuracy and generalization of the model, selecting the most useful parameters and a subset of features. The competitive advantages of the proposed method are shown through experimental results in comparison with the traditional neural networks. The suggested approach was repeated 30 times in a row and had the highest accuracy of 93.44 with the standard deviation of the result of 3.01 which is similar or even higher than other leading approaches. Our approach is more predictive of heart disease than multi-dataset. The research could contribute to making heart disease prediction more accurate so that more useful models can be used to benefit the medical industry.References
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