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Belief rule mining using the evidential reasoning rule for medical diagnosis. (English) Zbl 1487.68226

Summary: A belief rule mining approach is proposed to generate belief rules with a customized set of criteria by mining from multiple belief rules that are trained using data with varied sets of criteria. As the theoretical basis of the belief rule mining approach, the key concepts are defined, including the weights and reliabilities of cases, criteria, models, and belief rules. Based on the key concepts, multiple sub-models composed of belief rules with varied sets of criteria are initialized and optimized. Then, the optimized sub-models are integrated using the evidential reasoning rule into belief rules with a customized set of criteria. In the belief rule mining process, the weights and reliabilities of the sub-models are considered according to the weight and reliability calculation procedures of models proposed in this study. The proposed approach is used to help diagnose thyroid nodules with 527 medical cases, in which its applicability is demonstrated. By comparative experiments, the diagnostic correctness of the proposed approach is verified to be higher than those of the directly-optimized model and the approach without the consideration of reliability.

MSC:

68T37 Reasoning under uncertainty in the context of artificial intelligence
68T05 Learning and adaptive systems in artificial intelligence
68T30 Knowledge representation
92C50 Medical applications (general)

Software:

MADM
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Full Text: DOI

References:

[1] Denoeux, T., Decision-making with belief functions: a review, Int. J. Approx. Reason., 109, 87-110 (2019) · Zbl 1465.91038
[2] Xu, X. B.; Zheng, J.; Yang, J. B.; Xu, D. L.; Chen, Y. W., Data classification using evidence reasoning rule, Knowl.-Based Syst., 116, 144-151 (2017)
[3] Liu, Z. G.; Liu, Y.; Dezert, J.; Cuzzolin, F., Evidence combination based on credal belief redistribution for pattern classification, IEEE Trans. Fuzzy Syst., 28, 4, 618-631 (2019)
[4] Hossain, M. S.; Rahaman, S.; Mustafa, R.; Andersson, K., A belief rule-based expert system to assess suspicion of acute coronary syndrome (ACS) under uncertainty, Soft Comput., 22, 7571-7586 (2018)
[5] Kong, G. L.; Xu, D. L.; Yang, J. B.; Wang, T. B.; Jiang, B. G., Evidential reasoning rule-based decision support system for predicting ICU admission and in-hospital death of trauma, IEEE Trans. Syst. Man Cybern. Syst. (2020)
[6] Zhou, Z. G.; Liu, F.; Jiao, L. C.; Zhou, Z. J.; Yang, J. B.; Gong, M. G.; Zhang, X. P., A bi-level belief rule based decision support system for diagnosis of lymph node metastasis in gastric cancer, Knowl.-Based Syst., 54, 128-136 (2013)
[7] Biondi, B.; Filetti, S.; Schlumberger, M., Thyroid-hormone therapy and thyroid cancer: a reassessment, Nat. Rev. Endocrinol., 1, 32-40 (2005)
[8] Cabanillas, M. E.; McFadden, D. G.; Durante, C., Thyroid cancer, Lancet, 388, 2783-2795 (2016)
[9] Fu, C.; Chang, W. J.; Liu, W. Y.; Yang, S. L., Data-driven group decision making for diagnosis of thyroid nodule, Sci. China Inf. Sci., 62, Article 212205 pp. (2019)
[10] Little, R. J.; Rubin, D. B., Statistical Analysis with Missing Data (2019), John Wiley & Sons · Zbl 1411.62006
[11] García-Laencina, P. J.; Sancho-Gómez, J. L.; Figueiras-Vidal, A. R., Pattern classification with missing data: a review, Neural Comput. Appl., 19, 263-282 (2010)
[12] Schafer, J. L., Analysis of Incomplete Multivariate Data (1997), Chapman and Hall/CRC · Zbl 0997.62510
[13] Andridge, R. R.; Little, R. J.A., A review of hot deck imputation for survey non-response, Int. Stat. Rev., 78, 40-64 (2010)
[14] Donders, A. R.T.; Heijden, G. J.M. G.; Stijnen, T.; Moons, K. G.M., A gentle introduction to imputation of missing values, J. Clin. Epidemiol., 59, 1087-1091 (2006)
[15] Horton, N. J.; Lipsitz, S. R., Multiple imputation in practice: comparison of software packages for regression models with missing variables, Am. Stat., 55, 244-254 (2001)
[16] White, I. R.; Royston, P.; Wood, A. M., Multiple imputation using chained equations: issues and guidance for practice, Stat. Med., 30, 377-399 (2011)
[17] Chang, L. L.; Fu, C.; Wu, Z. J.; Liu, W. Y.; Yang, S. L., Data-driven analysis of radiologists’ behavior for diagnosing thyroid nodules, IEEE J. Biomed. Health Inform. (2020)
[18] Feng, Z. C.; Zhou, Z. J.; Hu, C. H.; Chang, L. L.; Hu, G. Y.; Zhao, F. J., A new belief rule base model with attribute reliability, IEEE Trans. Fuzzy Syst., 27, 5, 903-916 (2019)
[19] Si, X. S.; Hu, C. H.; Yang, J. B.; Zhou, Z. J., A new prediction model based on belief rule base for system’s behavior prediction, IEEE Trans. Fuzzy Syst., 19, 4, 636-651 (2011)
[20] Fu, C.; Liu, W. Y.; Chang, W. J., Data-driven multiple criteria decision making for diagnosis of thyroid cancer, Ann. Oper. Res., 1-30 (2018)
[21] Yang, J. B.; Xu, D. L., Evidential reasoning rule for evidence combination, Artif. Intell., 205, 1-29 (2013) · Zbl 1334.68225
[22] Yang, J. B.; Liu, J.; Wang, J.; Sii, H. S.; Wang, H. W., Belief rule-base inference methodology using the evidential reasoning approach-RIMER, IEEE Trans. Syst. Man Cybern., Part A, Syst. Hum., 36, 266-285 (2006)
[23] Chang, L. L.; Zhou, Y.; Jiang, J.; Li, M. J.; Zhang, X. H., Structure learning for belief rule base expert system: a comparative study, Knowl.-Based Syst., 39, 159-172 (2013)
[24] Yang, J. B.; Singh, M. G., An evidential reasoning approach for multiple-attribute decision making with uncertainty, IEEE Trans. Syst. Man Cybern., 24, 1-18 (1994)
[25] Zhang, A.; Gao, F.; Yang, M.; Bi, W., A new rule reduction and training method for extended belief rule base based on DBSCAN algorithm, Int. J. Approx. Reason., 119, 20-39 (2020) · Zbl 1434.68484
[26] Chang, L. L.; Chen, Y. W.; Hao, Z. Y.; Zhou, Z. J.; Xu, X. B.; Tan, X., Indirect disjunctive belief rule base modeling using limited conjunctive rules: two possible means, Int. J. Approx. Reason., 108, 1-20 (2019) · Zbl 1456.68188
[27] Hossain, M. S.; Zander, P. O.; Kamal, M. S.; Chowdhury, L., Belief-rule-based expert systems for evaluation of e-government: a case study, Expert Syst., 32, 5, 563-577 (2015)
[28] Wang, Y. M.; Yang, J. B.; Xu, D. L., Environmental impact assessment using the evidential reasoning approach, Eur. J. Oper. Res., 174, 1885-1913 (2006) · Zbl 1103.90364
[29] Kirch, W., Pearson’s correlation coefficient, (Encyclopedia of Public Health (2008), Springer: Springer Dordrecht)
[30] Jousselme, A. L.; Maupin, P., Distance in evidence theory: comprehensive survey and generalizations, Int. J. Approx. Reason., 53, 118-145 (2012) · Zbl 1280.68258
[31] James, A. P.; Dasarathy, B. V., Medical image fusion: a survey of the state of the art, Inf. Fusion, 19, 4-19 (2014)
[32] Moyano, J. M.; Gibaja, E. L.; Cios, K. J.; Ventura, S., An evolutionary approach to build ensembles of multi-label classifiers, Inf. Fusion, 50, 168-180 (2019)
[33] Moon, W. J.; Jung, S. L.; Lee, J. H.; Na, D.; Baek, J. H.; Lee, Y.; Kim, J.; Kim, H. S.; Byun, J. S.; Lee, D. H., Benign and malignant thyroid nodules: US differentiation—multicenter retrospective study, Radiology, 247, 762-770 (2008)
[34] Paschke, R.; Hegedüs, L.; Alexander, E.; Valcavi, R.; Papini, E.; Gharib, H., Thyroid nodule guidelines: agreement, disagreement and need for future research, Nat. Rev. Endocrinol., 7, 354 (2011)
[35] Simjanoska, M.; Kochev, S.; Tanevski, J.; Bogdanova, A. M.; Papa, G.; Eftimov, T., Multi-level information fusion for learning a blood pressure predictive model using sensor data, Inf. Fusion, 58, 24-39 (2020)
[36] Barnett, M. L.; Boddupalli, D.; Nundy, S.; Bates, D. W., Comparative accuracy of diagnosis by collective intelligence of multiple physicians vs individual physicians, JAMA Netw. Open, 2, 3, Article e190096 pp. (2019)
[37] Frates, M. C.; Benson, C. B.; Charboneau, J. W.; Cibas, E. S.; Clark, O. H.; Coleman, B. G.; Cronan, J. J.; Doubilet, P. M.; Evans, D. B.; Goellner, J. R.; Hay, I. D.; Hertzberg, B. S.; Intenzo, C. M.; Jeffrey, R. B.; Langer, J. E.; Larsen, P. R.; Mandel, S. J.; Middleton, W. D.; Reading, C. C.; Sherman, S. I.; Tessler, F. N., Management of thyroid nodules detected at US: society of radiologists in ultrasound consensus conference statement, Radiology, 237, 794-800 (2005)
[38] Filetti, S.; Durante, C.; Torlontano, M., Nonsurgical approaches to the management of thyroid nodules, Nat. Rev. Endocrinol., 2, 384 (2006)
[39] Kwak, J. Y.; Han, K. H.; Yoon, J. H.; Moon, H. J.; Son, E. J.; Park, S. H.; Jung, H. K.; Choi, J. S.; Kim, B. M.; Kim, E. K., Thyroid imaging reporting and data system for US features of nodules: a step in establishing better stratification of cancer risk, Radiology, 260, 892-899 (2011)
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