अमूर्त

Developing a new artificial neural network on sparse autoencoder for predicting heart disease.

Arabi Mariam*

Most medical datasets appear to be unbalanced, and when trained with such data, traditional machine learning algorithms underperform, particularly in the prediction of the minority class. This research proposes a strategy that consists of feature learning and classification stages that merge an enhanced sparse autoencoder (SAE) and Softmax regression, respectively, to overcome this difficulty and provide a robust model for disease prediction. Sparsity is achieved in the SAE network by penalising the network's weights, as opposed to traditional SAEs, which penalise the activations within the hidden layers. The Softmax classifier is further tuned for the classification task to attain great performance. As a result, the suggested method has the benefits of effective feature learning and reliable classification performance. When used to forecast three diseases, the suggested method achieved test accuracies of 98 percent, 97 percent, and 91 percent for chronic kidney disease, cervical cancer, and heart disease, respectively, outperforming other machine learning algorithms.