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Data-driven decision-making with weights and reliabilities for diagnosis of thyroid cancer

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Abstract

Data science has revolutionized the paradigms of medical decision-making. In the past, medical data could not be recorded and stored indefinitely. In the present day, huge volumes of medical data have been collected electronically, such as medical records, medical images, and heterogeneous surgical data. Under this condition, how to help the radiologists diagnose the thyroid cancer by using the accumulated examination reports and pathologic findings has been a challenge needing to face. From the analysis of historical examination reports, the problem of diagnosing thyroid cancer is evidently considered as a multi-criteria decision-making problem. Thus, a data-driven fusion method of weights and reliabilities in decision-making is proposed in this paper to cope with the above challenge. Linguistic term sets are introduced to model and portray the assessments on each criterion in the problem of diagnosing thyroid cancer by using three types of linguistic scale functions. A data-driven way is then designed to determine the weights and reliabilities of the assessments on each criterion for each radiologist by considering the similarity between the assessments on each criterion and the overall assessments and the similarity between the assessments on criterion and the golden standard, which are derived from the historical data. Subsequently, assessments on each criterion will be combined with the weights and reliabilities to generate a data-driven solution to the problem. The applicability and effectiveness of the data-driven fusion method are verified by solving a real problem of diagnosing thyroid cancer using historical data collected from five radiologists in a tertiary hospital from January 2011 to February 2019.

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Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Aggarwal M (2015) Generalized compensative weighted averaging aggregation operators. Comput Ind Eng 87:81–90

    Article  Google Scholar 

  2. Bao GY, Lian XL, He M, Wang LL (2010) Improved two-tuple linguistic representation model based on new linguistic evaluation scale. Control Decis 25(5):780–784 ((In Chinese))

    MathSciNet  Google Scholar 

  3. Blazquez D, Domenech J (2018) Big data sources and methods for social and economic analyses. Technol Forecast Soc 130:99–113

    Article  Google Scholar 

  4. Cappelli C, Castellano M, Pirola I, Cumetti D, Agosti B, Gandossi E, Rosei EA (2007) The predictive value of ultrasound findings in the management of thyroid nodules. QJM-Int J Med 100(1):29–35

    Article  Google Scholar 

  5. Chan BK, Desser TS, McDougall R, Weigel RJ, Jeffrey RB (2003) Common and uncommon sonographic features of papillary thyroid carcinoma. J Ultras Med 22(10):1083–1090

    Article  Google Scholar 

  6. Chen CLP, Zhang CY (2014) Data-intensive applications, challenges, techniques and technologies: a survey on big data. Inf Sci 275:314–347

    Article  Google Scholar 

  7. Chen YF, Yue XD, Fujita H, Fu SY (2017) Three-way decision support for diagnosis on focal liver lesions. Knowl-Based Syst 127:85–99

    Article  Google Scholar 

  8. Fu C, Chin KS (2014) Robust evidential reasoning approach with unknown attribute weights. Knowl-Based Syst 59:9–20

    Article  Google Scholar 

  9. Fu C, Wang YM (2015) An interval difference based evidential reasoning approach with unknown attribute weights and utilities of assessment grades. Comput Ind Eng 81:109–117

    Article  Google Scholar 

  10. Fu C, Xu DL (2016) Determining attribute weights to improve solution reliability and its application to selecting leading industries. Ann Oper Res 245(1–2):401–426

    Article  MathSciNet  MATH  Google Scholar 

  11. Fu C, Liu WY, Chang WJ (2020) Data-driven multiple criteria decision making for diagnosis of thyroid cancer. Ann Oper Res 293:833–862

    Article  MathSciNet  MATH  Google Scholar 

  12. Fu C, Chang WJ, Liu WY, Yang SL (2020) Data-driven selection of multi-criteria decision-making methods and its application to diagnosis of thyroid nodules. Comput Ind Eng 145:106490

    Article  Google Scholar 

  13. Fu C, Chang WJ, Xu DL, Yang SL (2019) An evidential reasoning approach based on criterion reliability and solution reliability. Comput Ind Eng 128:401–417

    Article  Google Scholar 

  14. Fu C, Chang WJ, Liu WY, Yang SL (2019) Data-driven group decision making for diagnosis of thyroid nodule. Sci China Inf Sci 62:212205

    Article  MathSciNet  Google Scholar 

  15. Ghasemeghaei M, Calic G (2019) Can big data improve firm decision quality? The role of data quality and data diagnosticity. Decis Support Syst 120:38–49

    Article  Google Scholar 

  16. Hasan M, Buyuktahtakin IE, Elamin E (2019) A multi-criteria ranking algorithm (MCRA) for determining breast cancer therapy. Omega 82:83–101

    Article  Google Scholar 

  17. Horvath E, Majlis S, Rossi R, Franco C, Niedmann JP, Castro A, Dominguez M (2009) An ultrasonogram reporting system for thyroid nodules stratifying cancer risk for clinical management. J Clin Endocr Metab 94(5):1748–1751

    Article  Google Scholar 

  18. Horvath E, Silva CF, Majlis S, Rodriguez I, Skoknic V, Castro A, Rojas H, Niedmann JP, Madrid A, Capdeville F, Whittle C, Rossi R, Dominguez M, Tala H (2017) Prospective validation of the ultrasound based TIRADS (Thyroid Imaging Reporting And Data System) classification: results in surgically resected thyroid nodules. Eur Radiol 27(6):2619–2628

    Article  Google Scholar 

  19. Huber J, Müller S, Fleischmann M, Stuckenschmidt H (2019) A data-driven newsvendor problem: from data to decision. Eur J Oper Res 278:904–915

    Article  MathSciNet  MATH  Google Scholar 

  20. Janis IL, Mann L (1977) Decision making: a psychological analysis of conflict, choice, and commitment. Free press, Berlin

    Google Scholar 

  21. Kwak JY, Han KH, Yoon JH, Moon HJ, Son EJ, Park SH, Jung HK, Choi JS, Kim BM, Kim EK (2011) Thyroid imaging reporting and data system for US features of nodules: a step in establishing better stratification of cancer risk. Radiology 260(3):892–899

    Article  Google Scholar 

  22. Kuo TC, Wu MH, Chen KY, Hsieh MS, Chen A, Chen CN (2020) Ultrasonographic features for differentiating follicular thyroid carcinoma and follicular adenoma. Asian J Surg 43(1):339–346

    Article  Google Scholar 

  23. Kovalchuk SV, Krotov E, Smirnov PA, Nasonov DA, Yakovlev AN (2018) Distributed data-driven platform for urgent decision making in cardiological ambulance control. Future Gener Comp SY 79(1):144–154

    Article  Google Scholar 

  24. Li X, Zhang SL, Zhang M, Liu H (2008) Rank of interval numbers based on a new distance measure. J X Univ (Nat Sci Ed) 27(1):87–90 ((In Chinese))

    Google Scholar 

  25. Li L (2009) Bottleneck detection of complex manufacturing systems using a data-driven method. Int J Prod Res 47(24):6929–6940

    Article  Google Scholar 

  26. Li ZW, Xie NX, Huang D, Zhang GQ (2020) A three-way decision method in a hybrid decision information system and its application in medical diagnosis. Artif Intell Rev 53(7):4707–4736

    Article  Google Scholar 

  27. Liao HC, Xu ZS, Zeng XJ, Merigó JM (2015) Qualitative decision making with correlation coefficient of hesitant fuzzy linguistic term sets. Knowl-Based Syst 76:127–138

    Article  Google Scholar 

  28. Liu PD, Wang P, Pedrycz W (2020) Consistency-and consensus-based group decision-making method with incomplete probabilistic linguistic preference relations. IEEE T Fuzzy Syst 29(9):2565–2579

    Article  Google Scholar 

  29. Liu PD, Teng F (2019) Probabilistic linguistic TODIM method for selecting products through online product reviews. Inf Sci 485:441–455

    Article  Google Scholar 

  30. Liu PD, Li Y (2019) Multi-attribute decision making method based on generalized maclaurin symmetric mean aggregation operators for probabilistic linguistic information. Comput Ind Eng 131:282–294

    Article  Google Scholar 

  31. Liu PD, Li Y (2019) A novel decision-making method based on probabilistic linguistic information. Cogn Comput 11(5):735–747

    Article  Google Scholar 

  32. Ma ZZ, Zhu JJ, Chen Y (2018) A probabilistic linguistic group decision-making method from a reliability perspective based on evidential reasoning. IEEE Trans Syst Man CY-S 50(7):2421–2435

    Article  Google Scholar 

  33. Malmir B, Amini M, Chang SI (2017) A medical decision support system for disease diagnosis under uncertainty. Expert Syst Appl 88:95–108

    Article  Google Scholar 

  34. Manco G, Ritacco E, Rullo P, Gallucci L, Astill W, Kimber D, Antoneli M (2017) Fault detection and explanation through big data analysis on sensor streams. Expert Syst Appl 87(30):141–156

    Article  Google Scholar 

  35. Merigó JM, Palacios-Marqués D, Aoto-Acosta P (2017) Distance measures, weighted averages, OWA operators and Bonferroni means. Appl Soft Comput 50:356–366

    Article  Google Scholar 

  36. Neilson A, Indratmo DB, Tjandra S (2019) Systematic review of the literature on big data in the transportation domain: concepts and applications. Big Data Res 17:35–44

    Article  Google Scholar 

  37. Qin JD, Liu XW, Pedrycz W (2007) An extended TODIM multi-criteria group decision making method for green supplier selection in interval type-2 fuzzy environment. Eur J Oper Res 258(2):626–638

    Article  MathSciNet  MATH  Google Scholar 

  38. Seib CD, Sosa JA (2019) Evolving understanding of the epidemiology of thyroid cancer. Endocrin Metab Clin 48(1):23–35

    Article  Google Scholar 

  39. Wang JQ, Wu JT, Wang JH, Zhang Y, Chen XH (2014) Interval-valued hesitant fuzzy linguistic sets and their applications in multi-criteria decision-making problems. Inf Sci 288:55–72

    Article  MathSciNet  MATH  Google Scholar 

  40. Wang JQ, Peng JJ, Zhang HY, Liu T, Chen XH (2015) An uncertain linguistic multi-criteria group decision-making method based on cloud model. Group Decis Negot 24(1):171–192

    Article  Google Scholar 

  41. Wang P, Liu PD, Chiclana F (2021) Multi-stage consistency optimization algorithm for decision making with incomplete probabilistic linguistic preference relation. Inf Sci 556:361–388

    Article  MathSciNet  MATH  Google Scholar 

  42. Wang P, Zhang PF, Li BW (2019) A three-way decision method based on Gaussian kernel in a hybrid information system with images: an application in medical diagnosis. Appl Soft Comput 77:734–749

    Article  Google Scholar 

  43. Xu ZS (2004) A method based on linguistic aggregation operators for group decision making with linguistic preference relations. Inf Sci 166:19–30

    Article  MathSciNet  MATH  Google Scholar 

  44. Xu DL (2012) An introduction and survey of the evidential reasoning approach for multiple criteria decision analysis. Ann Oper Res 195(1):163–187

    Article  MathSciNet  MATH  Google Scholar 

  45. Yao CH, Wu SD, Liu Z, Li P (2019) A deep learning model for predicting chemical composition of gallstones with big data in medical Internet of Things. Future Gener Comp SY 94:140–147

    Article  Google Scholar 

  46. Yao JT, Azam N (2014) Web-based medical decision support system for three-way medical decision making with game-theoretic rough sets. IEEE Trans Fuzzy Syst 23(1):3–15

    Article  Google Scholar 

  47. Yang JB, Xu DL (2002) On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty. IEEE Trans Syst MAN CY A 32(3):289–304

    Article  Google Scholar 

  48. Yang JB, Xu DL (2013) Evidential reasoning rule for evidence combination. Artif Intell 205:1–29

    Article  MathSciNet  MATH  Google Scholar 

  49. Yang Y, Hu JH, Liu YM, Chen XH (2019) A multiperiod hybrid decision support model for medical diagnosis and treatment based on similarities and three-way decision theory. Expert Syst 36(3):e12377

    Article  Google Scholar 

  50. Yu GF, Li DF, Fei W (2018) A novel method for heterogeneous multi-attribute group decision making with preference deviation. Comput Ind Eng 124:58–64

    Article  Google Scholar 

  51. Yu GF, Fei W, Li DF (2018) Compromise-typed variable weight decision method for hybrid multiattribute decision making. IEEE Trans Fuzzy Syst 27(5):861–872

    Article  Google Scholar 

  52. Yu GF, Li DF, Liang DC, Li GX (2021) An intuitionistic fuzzy multi-objective goal programming approach to portfolio selection. Int J Inf Tech Decis 2021:1–21

    Google Scholar 

  53. Zhou M, Liu XB, Chen YW, Yang JB (2018) Evidential reasoning rule for MADM with both weights and reliabilities in group decision making. Knowl-Based Syst 143:142–161

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by the National Natural Science Foundation of China (Grant nos. 72001063, 72071056 and 71571060).

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Authors and Affiliations

  1. School of Management, Hefei University of Technology, Hefei, Box 270, Hefei, 230009, Anhui, People’s Republic of China

    Min Xue, Peipei Cao & Bingbing Hou

  2. Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei, Box 270, Hefei, 230009, Anhui, People’s Republic of China

    Min Xue, Peipei Cao & Bingbing Hou

  3. Division of Life Sciences and Medicine, Department of Ultrasound, The First Affiliated Hospital of USTC, University of Science and Technology of China, Hefei, 230001, China

    Weiyong Liu

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  1. Min Xue
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Correspondence to Min Xue or Weiyong Liu.

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Xue, M., Cao, P., Hou, B. et al. Data-driven decision-making with weights and reliabilities for diagnosis of thyroid cancer. Int. J. Mach. Learn. & Cyber. 13, 2257–2271 (2022). https://doi.org/10.1007/s13042-022-01521-x

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  • DOI: https://doi.org/10.1007/s13042-022-01521-x

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