Abstract
Data intensive technologies using medical big data, analysed by machine learning algorithms, play a key role in revolutionising healthcare. However, results from several findings show that these algorithms have potential to gain negative impact on healthcare system as compared to the existing primitive healthcare systems which involve physicians. Current algorithms are accused of these deficiencies resulting from biased training data bearing numerous missing values, errors, and biased inputs. This is due to under- or over-representation of certain groups of data, trivial data curation methods, etc. In this chapter, we describe Perceptive Bias, Processing Bias, and the ways to compute bias for Medical Big Data analysis.
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Saxena, A., Saxena, M., Ilerena, A.R. (2021). Bias in Medical Big Data and Machine Learning Algorithms. In: Saxena, A., Chandra, S. (eds) Artificial Intelligence and Machine Learning in Healthcare . Springer, Singapore. https://doi.org/10.1007/978-981-16-0811-7_10
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DOI: https://doi.org/10.1007/978-981-16-0811-7_10
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