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A Surrogate Data-Based Approach for Validating Deep Learning Model Used in Healthcare

A Surrogate Data-Based Approach for Validating Deep Learning Model Used in Healthcare

Meenakshi Srivastava
Copyright: © 2020 |Pages: 15
ISBN13: 9781799821014|ISBN10: 1799821013|EISBN13: 9781799821021
DOI: 10.4018/978-1-7998-2101-4.ch009
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MLA

Srivastava, Meenakshi. "A Surrogate Data-Based Approach for Validating Deep Learning Model Used in Healthcare." Applications of Deep Learning and Big IoT on Personalized Healthcare Services, edited by Ritika Wason, et al., IGI Global, 2020, pp. 132-146. https://doi.org/10.4018/978-1-7998-2101-4.ch009

APA

Srivastava, M. (2020). A Surrogate Data-Based Approach for Validating Deep Learning Model Used in Healthcare. In R. Wason, D. Goyal, V. Jain, S. Balamurugan, & A. Baliyan (Eds.), Applications of Deep Learning and Big IoT on Personalized Healthcare Services (pp. 132-146). IGI Global. https://doi.org/10.4018/978-1-7998-2101-4.ch009

Chicago

Srivastava, Meenakshi. "A Surrogate Data-Based Approach for Validating Deep Learning Model Used in Healthcare." In Applications of Deep Learning and Big IoT on Personalized Healthcare Services, edited by Ritika Wason, et al., 132-146. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2101-4.ch009

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Abstract

IoT-based communication between medical devices has encouraged the healthcare industry to use automated systems which provide effective insight from the massive amount of gathered data. AI and machine learning have played a major role in the design of such systems. Accuracy and validation are considered, since copious training data is required in a neural network (NN)-based deep learning model. This is hardly feasible in medical research, because the size of data sets is constrained by complexity and high cost experiments. The availability of limited sample data validation of NN remains a concern. The prediction of outcomes on a NN trained on a smaller data set cannot guarantee performance and exhibits unstable behaviors. Surrogate data-based validation of NN can be viewed as a solution. In the current chapter, the classification of breast tissue data by a NN model has been detailed. In the absence of a huge data set, a surrogate data-based validation approach has been applied. The discussed study can be applied for predictive modelling for applications described by small data sets.

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