Discussion on Comparing Machine Learning Models for Health Outcome Prediction

Janusz Wojtusiak, Negin Asadzadehzanjani

2022

Abstract

This position paper argues the need for more details than simple statistical accuracy measures when comparing machine learning models constructed for patient outcome prediction. First, statistical accuracy measures are briefly discussed, including AROC, APRC, predictive accuracy, precision, recall, and their variants. Then, model correlation plots are introduced that compare outputs from two models. Finally, a more detailed analysis of inputs to the models is presented. The discussions are illustrated with two classification problems in predicting patient mortality and high utilization of medical services.

Paper Citation

in Harvard Style

Wojtusiak J. and Asadzadehzanjani N. (2022). Discussion on Comparing Machine Learning Models for Health Outcome Prediction. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF; ISBN 978-989-758-552-4, SciTePress, pages 711-718. DOI: 10.5220/0010916600003123

in Bibtex Style

@conference{healthinf22,
author={Janusz Wojtusiak and Negin Asadzadehzanjani},
title={Discussion on Comparing Machine Learning Models for Health Outcome Prediction},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF},
year={2022},
pages={711-718},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010916600003123},
isbn={978-989-758-552-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 5: HEALTHINF
TI - Discussion on Comparing Machine Learning Models for Health Outcome Prediction
SN - 978-989-758-552-4
AU - Wojtusiak J.
AU - Asadzadehzanjani N.
PY - 2022
SP - 711
EP - 718
DO - 10.5220/0010916600003123
PB - SciTePress