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Cost-Sensitive Machine Learning Classification for Mass Tuberculosis Screening

2019-06-28 health informatics Title + abstract only
View on medRxiv
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Active screening for Tuberculosis (TB) is needed to optimize detection and treatment. However, current algorithms for verbal screening perform poorly, causing misclassification that leads to missed cases and unnecessary and costly laboratory tests for false positives. We investigated the role of machine learning to improve the predefined one-size-fits-all algorithm used for scoring the verbal screening questionnaire. We present a cost-sensitive machine learning classification for mass tuberculos...

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