Prediction models to detect patients with heart diseases using neural networks and the Pytorch and TensorFlow libraries
DOI:
https://doi.org/10.36825/RITI.12.26.010Keywords:
Heart Disease, Neural Networks, Pytorch, TensorFlow, Prediction ModelsAbstract
Neural Networks are used to generate prediction models aimed at determining whether an individual has heart conditions. The PyTorch and TensorFlow libraries are applied to a dataset of patients related to heart diseases, containing 17 predictor variables. The purpose of this work is to compare the results obtained using the aforementioned libraries with Neural Networks, analyzing the behavior of loss functions and the outcomes from the confusion matrix when creating the prediction model. DownSampling and UpSampling techniques are employed to address the imbalance in the dataset, which consists of a total of 319,795 patients, of whom only 27,373 have heart disease. It was found that for this dataset, the best results with PyTorch are achieved in models of 100 epochs and above, with execution times of only a few seconds, while TensorFlow shows good results starting from models with 10 epochs, though its execution time is considerably longer. An analysis is conducted on the difference in computation time between PyTorch and TensorFlow.
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