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Currently submitted to: JMIR Preprints

Date Submitted: Aug 30, 2024

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

dummyML: Automated tabular data analysis pipelines for non-experts

  • Yipeng Song; 
  • Yang S Liu; 
  • Dan Metes; 
  • Mengzhe Wang; 
  • Bo Cao

ABSTRACT

Health researchers and healthcare professionals are interested in exploring predictive analytics and machine learning applications of health data but were challenged by accessing software programming expertise and dealing with the complexity of carrying out machine learning based predictive analysis (e.g., prediction of a future outcome at the individual level). We present an automated machine learning analytical pipeline dummyML, a free and open-source tool designed to reduce the burden of conducting exploratory machine-learning data analysis on tabular data (e.g., data organized into a table, in which information is arranged in rows and columns), for non-experts, and to facilitate meaningful variable explanation.


 Citation

Please cite as:

Song Y, Liu YS, Metes D, Wang M, Cao B

dummyML: Automated tabular data analysis pipelines for non-experts

JMIR Preprints. 30/08/2024:65966

DOI: 10.2196/preprints.65966

URL: https://preprints.jmir.org/preprint/65966

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