Data Domain Change and Feature Selection to Predict Cardiac Pathology with a 2D Clinical Dataset and Convolutional Neural Networks (Student Abstract)
Keywords:Machine Learning, Applications, Feature Selection
AbstractThis work discusses a strategy named Map, Optimize and Learn (MOL) which analyzes how to change the representation of samples of a 2D dataset to generate useful patterns for classification tasks using Convolutional Neural Networks (CNN) architectures. The strategy is applied to a real-world scenario of children and teenagers with cardiac pathology and compared against state of the art Machine Learning (ML) algorithms for 2D datasets. Preliminary results suggests that the strategy has potential to improve the prediction quality.
How to Cite
Serra Neto, M., Mollinetti, M., & Dutra, I. (2021). Data Domain Change and Feature Selection to Predict Cardiac Pathology with a 2D Clinical Dataset and Convolutional Neural Networks (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15883-15884. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17938
AAAI Student Abstract and Poster Program