Abstract
In this tutorial, we will provide an introduction to the main Python software tools used for applying machine learning techniques to medical data. The focus will be on open-source software that is freely available and is cross platform. To aid the learning experience, a companion GitHub repository is available so that you can follow the examples contained in this paper interactively using Jupyter notebooks. The notebooks will be more exhaustive than what is contained in this chapter, and will focus on medical datasets and healthcare problems. Briefly, this tutorial will first introduce Python as a language, and then describe some of the lower level, general matrix and data structure packages that are popular in the machine learning and data science communities, such as NumPy and Pandas. From there, we will move to dedicated machine learning software, such as SciKit-Learn. Finally we will introduce the Keras deep learning and neural networks library. The emphasis of this paper is readability, with as little jargon used as possible. No previous experience with machine learning is assumed. We will use openly available medical datasets throughout.
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Notes
- 1.
To speed up certain numerical operations, the numexpr and bottleneck optimised libraries for Python can be installed. These are included in the Anaconda distribution, readers who are not using Anaconda are recommended to install them both.
- 2.
Users of MATLAB may want to view this excellent guide to NumPy for MATLAB users: http://mathesaurus.sourceforge.net/matlab-numpy.html.
- 3.
See http://pandas.pydata.org/pandas-docs/stable/missing_data.html for more methods on handling missing data.
- 4.
For some metrics, see the Keras author’s tweet: https://twitter.com/fchollet/status/765212287531495424.
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We would like to thank the two reviewers for their suggestions and input which helped improve this tutorial.
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Bloice, M.D., Holzinger, A. (2016). A Tutorial on Machine Learning and Data Science Tools with Python. In: Holzinger, A. (eds) Machine Learning for Health Informatics. Lecture Notes in Computer Science(), vol 9605. Springer, Cham. https://doi.org/10.1007/978-3-319-50478-0_22
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