Data Domain Change and Feature Selection to Predict Cardiac Pathology with a 2D Clinical Dataset and Convolutional Neural Networks (Student Abstract)

Mario Tasso Ribeiro Serra Neto, Marco A. F. Mollinetti, Inês Dutra

AAAI 2021 pp. 15883-15884

doi:10.1609/AAAI.V35I18.17938 /aaai/2021/neto2021aaai-data/

Abstract

This 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.

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Text

Neto et al. "Data Domain Change and Feature Selection to Predict Cardiac Pathology with a 2D Clinical Dataset and Convolutional Neural Networks (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I18.17938

Markdown

[Neto et al. "Data Domain Change and Feature Selection to Predict Cardiac Pathology with a 2D Clinical Dataset and Convolutional Neural Networks (Student Abstract)." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/neto2021aaai-data/) doi:10.1609/AAAI.V35I18.17938

BibTeX

@inproceedings{neto2021aaai-data,
  title     = {{Data Domain Change and Feature Selection to Predict Cardiac Pathology with a 2D Clinical Dataset and Convolutional Neural Networks (Student Abstract)}},
  author    = {Neto, Mario Tasso Ribeiro Serra and Mollinetti, Marco A. F. and Dutra, Inês},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2021},
  pages     = {15883-15884},
  doi       = {10.1609/AAAI.V35I18.17938},
  url       = {https://mlanthology.org/aaai/2021/neto2021aaai-data/}
}