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Voodoo Machine Learning for Clinical Predictions

View ORCID ProfileSohrab Saeb, Luca Lonini, Arun Jayaraman, David C. Mohr, View ORCID ProfileKonrad P. Kording
doi: https://doi.org/10.1101/059774
Sohrab Saeb
aDepartment of Preventive Medicine, Northwestern University, Chicago, USA
bDepartment of Physical Medicine and Rehabilitation, Northwestern University, USA
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Luca Lonini
bDepartment of Physical Medicine and Rehabilitation, Northwestern University, USA
cMax Nader Lab for Rehabilitation Technologies and Outcomes Research, Rehabilitation Institute of Chicago, Chicago, USA
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Arun Jayaraman
bDepartment of Physical Medicine and Rehabilitation, Northwestern University, USA
cMax Nader Lab for Rehabilitation Technologies and Outcomes Research, Rehabilitation Institute of Chicago, Chicago, USA
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David C. Mohr
aDepartment of Preventive Medicine, Northwestern University, Chicago, USA
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Konrad P. Kording
bDepartment of Physical Medicine and Rehabilitation, Northwestern University, USA
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Abstract

The availability of smartphone and wearable sensor technology is leading to a rapid accumulation of human subject data, and machine learning is emerging as a technique to map that data into clinical predictions. As machine learning algorithms are increasingly used to support clinical decision making, it is important to reliably quantify their prediction accuracy. Cross-validation is the standard approach for evaluating the accuracy of such algorithms; however, several cross-validations methods exist and only some of them are statistically meaningful. Here we compared two popular cross-validation methods: record-wise and subject-wise. Using both a publicly available dataset and a simulation, we found that record-wise cross-validation often massively overestimates the prediction accuracy of the algorithms. We also found that this erroneous method is used by almost half of the retrieved studies that used accelerometers, wearable sensors, or smartphones to predict clinical outcomes. As we move towards an era of machine learning based diagnosis and treatment, using proper methods to evaluate their accuracy is crucial, as erroneous results can mislead both clinicians and data scientists.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted June 19, 2016.
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Voodoo Machine Learning for Clinical Predictions
Sohrab Saeb, Luca Lonini, Arun Jayaraman, David C. Mohr, Konrad P. Kording
bioRxiv 059774; doi: https://doi.org/10.1101/059774
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Voodoo Machine Learning for Clinical Predictions
Sohrab Saeb, Luca Lonini, Arun Jayaraman, David C. Mohr, Konrad P. Kording
bioRxiv 059774; doi: https://doi.org/10.1101/059774

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