Analytics of Heterogeneous Breast Cancer Data Using Neuroevolution

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dc.contributor.authorAbdikenov, Beibit
dc.contributor.authorIklassov, Zangir
dc.contributor.authorSharipov, Askhat
dc.contributor.authorHussain, Shahid
dc.contributor.authorJamwal, Prashant K.
dc.date.accessioned2019-12-11T03:54:33Z
dc.date.available2019-12-11T03:54:33Z
dc.date.issued2019-02-01
dc.descriptionhttps://ieeexplore.ieee.org/document/8632897en_US
dc.description.abstractBreast cancer prognostic modeling is difficult since it is governed by many diverse factors. Given the low median survival and large scale breast cancer data, which comes from high throughput technology, the accurate and reliable prognosis of breast cancer is becoming increasingly difficult. While accurate and timely prognosis may save many patients from going through painful and expensive treatments, it may also help oncologists in managing the disease more efficiently and effectively. Data analytics augmented by machine-learning algorithms have been proposed in past for breast cancer prognosis; and however, most of these could not perform well owing to the heterogeneous nature of available data and model interpretability related issues. A robust prognostic modeling approach is proposed here whereby a Pareto optimal set of deep neural networks (DNNs) exhibiting equally good performance metrics is obtained. The set of DNNs is initialized and their hyperparameters are optimized using the evolutionary algorithm, NSGAIII. The final DNN model is selected from the Pareto optimal set of many DNNs using a fuzzy inferencing approach. Contrary to using DNNs as the black box, the proposed scheme allows understanding how various performance metrics (such as accuracy, sensitivity, F1, and so on) change with changes in hyperparameters. This enhanced interpretability can be further used to improve or modify the behavior of DNNs. The heterogeneous breast cancer database requires preprocessing for better interpretation of categorical variables in order to improve prognosis from classifiers. Furthermore, we propose to use a neural network-based entity-embedding method for categorical features with high cardinality. This approach can provide a vector representation of categorical features in multidimensional space with enhanced interpretability. It is shown with evidence that DNNs optimized using evolutionary algorithms exhibit improved performance over other classifiers mentioned in this paper.en_US
dc.identifier.citationAbdikenov, B., Iklassov, Z., Sharipov, A., Hussain, S., & Jamwal, P. K. (2019). Analytics of Heterogeneous Breast Cancer Data Using Neuroevolution. IEEE Access, 7, 18050–18060. https://doi.org/10.1109/access.2019.2897078en_US
dc.identifier.issn2169-3536
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/4341
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectBreast cancer prognostic modellingen_US
dc.subjectentity embeddingen_US
dc.subjectdeep learning networksen_US
dc.subjectevolutionary algorithmsen_US
dc.subjectfuzzy inferencingen_US
dc.titleAnalytics of Heterogeneous Breast Cancer Data Using Neuroevolutionen_US
dc.typeArticleen_US
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