A Random Forest a Day Keeps the Doctor Away
Markus Eyting ()
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Markus Eyting: Johannes Gutenberg University
No 2026, Working Papers from Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz
Using a unique dataset from a German health check-up provider including detailed individual questionnaire data as well as medical test data, I apply a random forest to predict several health risk factors. I evaluate the prediction performance using various metrics and find decent prediction qualities across all outcomes. By identifying the most relevant predictor variables, I compile concise and validated questionnaire tools to identify individuals’ blood pressure, blood glucose, and cholesterol levels, their risk of a coronary heart disease, whether or not they suffer from plaque or a metabolic syndrome as well as their relative fitness levels. In a second step, I compare the prediction results to physician predictions of the same patient observations. I find that the random forest outperforms the physicians if predictions are based on the same information set. When additionally providing the physicians with the random forest predictions for a particular patient observation, the physicians align with the random forest predictions. Finally, while the random forest considers various psychological scales, the physicians focus on family health history information instead.
Pages: 85 pages
New Economics Papers: this item is included in nep-big, nep-env, nep-eur and nep-hea
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https://download.uni-mainz.de/RePEc/pdf/Discussion_Paper_2026.pdf First version, 2020 (application/pdf)
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Persistent link: https://EconPapers.repec.org/RePEc:jgu:wpaper:2026
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