Title

Session 10: Healthcare - Deep Neural Networkds to Predict Self-perception of Cardiovascular Disease: A Technical Demo

Presenter Information/ Coauthors Information

David Zeng, Dakota State University

Presentation Type

Event

Abstract

I train a multiple-hidden-layer deep neural network that predicts self-perception of heart health (including Cardiovascular Disease) with a large data set of 1729 features and about 30,000 samples. The dataset is based on the CDC Demographics, Dietary, Examination, Laboratory, and Questionnaire datasets collected from 1999 to 2016. Substantial data cleaning and pre-processing are done with Python pandas library. The objective of this research is three-fold:

  • Better understanding of how well DNN would improve the accuracy of prediction on perception of cardiovascular disease;
  • Framework of developing more sophisticated DNN models to predict medical outcomes;
  • Foundation for learning multi-dimensional/distributed representation of healthcare concepts that are both interpretable and scalable.

The presentation focuses on the technical (training a deep neural network with latest developments in the field of deep learning) aspects of the research.

Start Date

12-2-2018 3:30 PM

End Date

12-2-2018 5:00 PM

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Feb 12th, 3:30 PM Feb 12th, 5:00 PM

Session 10: Healthcare - Deep Neural Networkds to Predict Self-perception of Cardiovascular Disease: A Technical Demo

University Student Union: Pheasant Room 253 A/B

I train a multiple-hidden-layer deep neural network that predicts self-perception of heart health (including Cardiovascular Disease) with a large data set of 1729 features and about 30,000 samples. The dataset is based on the CDC Demographics, Dietary, Examination, Laboratory, and Questionnaire datasets collected from 1999 to 2016. Substantial data cleaning and pre-processing are done with Python pandas library. The objective of this research is three-fold:

  • Better understanding of how well DNN would improve the accuracy of prediction on perception of cardiovascular disease;
  • Framework of developing more sophisticated DNN models to predict medical outcomes;
  • Foundation for learning multi-dimensional/distributed representation of healthcare concepts that are both interpretable and scalable.

The presentation focuses on the technical (training a deep neural network with latest developments in the field of deep learning) aspects of the research.