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Machine Learning—Automated Machine Learning (AutoML) for Disease Prediction

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Clinical Applications of Artificial Intelligence in Real-World Data

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Abstract

The selection and tuning of feature selection, feature engineering, and classification or regression algorithms is a major challenge in machine learning, affecting both beginners and experts. Automated machine learning (AutoML) offers a solution by automating the creation of machine learning pipelines, eliminating the guesswork associated with a manual process. This chapter reviews the challenges of building pipelines and introduces some of the most widely used AutoML methods and open-source software. We focus on TPOT, an AutoML method that utilizes genetic programming for discovery and optimization and represents pipelines as expression trees. We also explore TPOT extensions and its use in handling biomedical big data.

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Authors and Affiliations

  1. Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Jason H. Moore, Pedro H. Ribeiro, Nicholas Matsumoto & Anil K. Saini

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  1. Jason H. Moore
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  2. Pedro H. Ribeiro
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  3. Nicholas Matsumoto
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  4. Anil K. Saini
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Corresponding author

Correspondence to Jason H. Moore .

Editor information

Editors and Affiliations

  1. Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands

    Folkert W. Asselbergs

  2. Institute of Health Informatics, University College London, London, UK

    Spiros Denaxas

  3. Department of Data Science and Biostatistics, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands

    Daniel L. Oberski

  4. Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Jason H. Moore

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Moore, J.H., Ribeiro, P.H., Matsumoto, N., Saini, A.K. (2023). Machine Learning—Automated Machine Learning (AutoML) for Disease Prediction. In: Asselbergs, F.W., Denaxas, S., Oberski, D.L., Moore, J.H. (eds) Clinical Applications of Artificial Intelligence in Real-World Data. Springer, Cham. https://doi.org/10.1007/978-3-031-36678-9_10

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  • DOI: https://doi.org/10.1007/978-3-031-36678-9_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-36677-2

  • Online ISBN: 978-3-031-36678-9

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