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الرئيسية > A Hybrid Bayesian Network And...>الوصف

A Hybrid Bayesian Network And Tensor Factorization Approach For Missing Value Imputation To Improve Breast Cancer Recurrence Prediction

المصدر: مجلة جامعة الملك سعود - علوم الحاسب والمعلومات
الناشر: جامعة الملك سعود
المؤلف الرئيسي: Vazifehdan, Mahin (Author)
مؤلفين آخرين: Moattar, Mohammad Hossein (Co-Author), Jalali, Mehrdad (Co-Author)
المجلد/العدد: مج31, ع2
محكمة: نعم
الدولة: السعودية
التاريخ الميلادي: 2019
الصفحات: 175 - 184
DOI: 10.33948/0584-031-002-004
ISSN: 1319-1578
رقم MD: 974600
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: science
مواضيع:
تشخيص الأمراض | سرطان الثدي | المعلومات الطبية
كلمات المؤلف المفتاحية:
Breast Cancer Recurrence | Missing Value Imputation | Classification | Tensor Factorization | Bayesian Network
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المستخلص: Data mining and machine learning approaches can be used to predict breast cancer recurrence. However, real datasets often include missing values for various reasons. In this paper, a hybrid imputation method is proposed with respect to the dependency between the attributes and the type of incomplete attributes in order to especially improve the prediction of breast cancer recurrence. After splitting the dataset into two discrete and numerical subsets, first missing values of the discrete fields are imputed using Bayesian network. Then, using Tensor factorization, the integrated dataset, which comprises of the filled-subset of the previous stage and numerical missing values subset, is constructed so that both continuous missing values are imputed and the accuracy of imputation is enhanced. We evaluated the proposed method versus six imputation methods i.e. mean, Hot-deck, K-NN, Weighted K-NN, Tensor factorization and Bayesian network on three datasets and used three classifiers, namely decision tree, K-Nearest Neighbor and Support Vector Machine for recurrence prediction. Experimental results show that the proposed method has as average 0.26 prediction improvement. Also, the prediction performance of the proposed approach outperforms all other imputation-classifier pairs in terms of specificity, sensitivity and accuracy. © 2018 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
ISSN: 1319-1578

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