Conditional Generative Adversarial Network (CGAN) Based Data Oversampling And Gaussian Bobcat Optimization Algorithm (GBOA) For Heart Diseases Prediction
Abstract
Early detection of cardiac problems and continuous patient monitoring by physicians can help reduce death rates. The classification of imbalanced datasets is an important task in machine learning. The number of samples in each class is not uniformly distributed; one class contains a large number of samples while the other has a small number. This often leads to higher classification accuracy for the majority category and lower classification accuracy for the minority category. In this paper, Conditional Generative Adversarial Network (CGAN) is a type of neural network to generate new data samples similar to a given training sample. CGAN is introduced to translate unbalanced samples from one form to another resulting in balanced samples. Gaussian Bobcat Optimization Algorithm (GBOA) is introduced for the importance of features and provides valuable insights into the relevance and predictive power of each feature in a heart disease dataset. GBOA is derived from the hunting strategy of bobcats during the attack towards the prey and the chase process between them. Natural behaviors of the bobcat in the wild, the strategy of this animal during hunting is much more prominent. Memory-Augmented Deep Autoencoder (MADAE) classifier consists of a fully connected three-layer neural network where the encoder contains input and hidden layers and the decoder part comprises hidden and output layers. By allowing early detection and treatment, accurate heart disease prediction can greatly reduce mortality. Cleveland Heart Disease Database (CHDD) is collected from University of California Irvine (UCI) repository. Experimental results show the effectiveness of the classifiers in terms of Precision, Recall, F-Measure, and Accuracy.
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