Exploring computational methods in the statistical analysis of imprecise medical data: between epistemology and ontology

Authors

DOI:

https://doi.org/10.56294/saludcyt20241341

Keywords:

Statistics, Computational Methods, Imprecise Data, Uncertainty, Epistemology, Ontology

Abstract

The accuracy of the results is essential to evaluate the effectiveness of statistical methods in the analysis of medical data with uncertainty. Indicators such as margin of error, percent agreement and coefficient of determination quantified accuracy under epistemic and ontological uncertainty. The stability of the methods was assessed by variation in trend analysis, sensitivity to small variations and model robustness. Data reliability focused on the selection of methods that effectively handle epistemic uncertainty, recording assumptions, sensitivity analysis and internal consistency. Ontological imprecision was quantified using the fuzzy membership degree and the overlap coefficient. The exploration of computational methods underlined the importance of accuracy and the handling of epistemic and ontological uncertainty, ensuring reliable results. The geometric mean filter, with a score of 0,7790, stood out as the best for its accuracy and ability to effectively handle uncertainty

References

1. Maghrabie, H., Beauregard, Y., & Schiffauerova, A. Grey-based Multi-Criteria Decision Analysis approach: Addressing uncertainty at complex decision problems. Technological Forecasting and Social Change. 2019; 146:366-379. https://doi.org/10.1016/J.TECHFORE.2019.05.031

2. Keith, A., & Ahner, D. A survey of decision making and optimization under uncertainty. Annals of Operations Research. 2019; 300:319-353. https://doi.org/10.1007/s10479-019-03431-8

3. Hewitt, M., Ortmann, J., & Rei, W. Decision-based scenario clustering for decision-making under uncertainty. Annals of Operations Research. 2021;1-25. https://doi.org/10.1007/s10479-020-03843-x

4. Moroni, S., & Chiffi, D. Uncertainty and Planning: Cities, Technologies and Public Decision-Making. Perspectives on Science. 2022; 30:237-259. https://doi.org/10.1162/posc_a_00413

5. Hinkel, J., Feyen, L., Hemer, M., Cozannet, G., Lincke, D., Marcos, M., Mentaschi, L., Merkens, J., Moel, H., Muis, S., Nicholls, R., Vafeidis, A., Wal, R., Vousdoukas, M., Wahl, T., Ward, P., & Wolff, C. Uncertainty and Bias in Global to Regional Scale Assessments of Current and Future Coastal Flood Risk. Earth's Future. 2021;9. https://doi.org/10.1029/2020EF001882

6. Ng, S., Faraji-Rad, A., & Batra, R. Uncertainty Evokes Consumers’ Preference for Brands Incongruent with their Global–Local Citizenship Identity. Journal of Marketing Research. 2020; 58:400-415. https://doi.org/10.1177/0022243720972956

7. Herran, D., Tachiiri, K., & Matsumoto, K. Global energy system transformations in mitigation scenarios considering climate uncertainties. Applied Energy. 2019; 243:119-131. https://doi.org/10.1016/J.APENERGY.2019.03.069

8. Afanador Cubillos N. Historia de la producción y sus retos en la era actual. Región Científica. 2023;2(1):202315. https://doi.org/10.58763/rc202315

9. Stefan, A. Statistics for Making Decisions. The American Statistician. 2022; 76:87-88. https://doi.org/10.1080/00031305.2021.2020003

10. Roman-Acosta D, Rodríguez-Torres E, Baquedano-Montoya MB, López-Zavala L, Pérez-Gamboa AJ. ChatGPT y su uso para perfeccionar la escritura académica en educandos de posgrado. Praxis Pedagógica. 2024;24(36):53-75. https://revistas.uniminuto.edu/index.php/praxis/article/view/3536

11. Hassani, H., Beneki, C., Silva, E., Vandeput, N., & Madsen, D. The science of statistics versus data science: What is the future? Technological Forecasting and Social Change. 2021; 173:121111. https://doi.org/10.1016/J.TECHFORE.2021.121111

12. Kammerer-David MI, Murgas-Téllez B. La innovación tecnológica desde un enfoque de dinámica de sistemas. Región Científica. 2024;3(1):2024217. https://doi.org/10.58763/rc2024217

13. Chicco D, Shiradkar R. Ten quick tips for computational analysis of medical images. PLOS Computational Biology. 2023;19. https://doi.org/10.1371/journal.pcbi.1010778

14. Barisoni L, Lafata K, Hewitt S, Madabhushi A, Balis U. Digital pathology and computational image analysis in nephropathology. Nature Reviews Nephrology. 2020;16:669-685. https://doi.org/10.1038/s41581-020-0321-6

15. Sengupta K, Srivastava P. Quantum algorithm for quicker clinical prognostic analysis: an application and experimental study using CT scan images of COVID-19 patients. BMC Medical Informatics and Decision Making. 2021;21. https://doi.org/10.1186/s12911-021-01588-6

16. Liu Z, Wang S, Dong D, Wei J, Fang C, Zhou X, et al. The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges. Theranostics. 2019;9:1303-1322. https://doi.org/10.7150/thno.30309

17. Abdar M, Pourpanah F, Hussain S, Rezazadegan D, Liu L, Ghavamzadeh M, et al. A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges. Information Fusion. 2020;76:243-297. https://doi.org/10.1016/j.inffus.2021.05.008

18. Wang X, Yao L, Wang X, Paik H, Wang S. Uncertainty Estimation With Neural Processes for Meta-Continual Learning. IEEE Transactions on Neural Networks and Learning Systems. 2022;34:6887-6897. https://doi.org/10.1109/TNNLS.2022.3215633

19. Al-turjman F, Zahmatkesh H, Mostarda L. Quantifying Uncertainty in Internet of Medical Things and Big-Data Services Using Intelligence and Deep Learning. IEEE Access. 2019;7:115749-115759. https://doi.org/10.1109/ACCESS.2019.2931637

20. Herzog L, Murina E, Dürr O, Wegener S, Sick B. Integrating uncertainty in deep neural networks for MRI based stroke analysis. Medical image analysis. 2020;65:101790. https://doi.org/10.1016/j.media.2020.101790

21. Ghesu F, Georgescu B, Mansoor A, Yoo Y, Gibson E, Vishwanath R, et al. Quantifying and Leveraging Predictive Uncertainty for Medical Image Assessment. Medical image analysis. 2020; 68:101855. https://doi.org/10.1016/j.media.2020.101855

22. Rajaraman S, Zamzmi G, Yang F, Xue Z, Jaeger S, Antani S. Uncertainty Quantification in Segmenting Tuberculosis-Consistent Findings in Frontal Chest X-rays. Biomedicines. 2022;10. https://doi.org/10.3390/biomedicines10061323

23. Sathiyamoorthi V, Ilavarasi A, Murugeswari K, Ahmed S, Devi B, Kalipindi M. A deep convolutional neural network based computer aided diagnosis system for the prediction of Alzheimer's disease in MRI images. Measurement. 2020; 171:108838. https://doi.org/10.1016/j.measurement.2020.108838

24. Cabeli V, Verny L, Sella N, Uguzzoni G, Verny M, Isambert H. Learning clinical networks from medical records based on information estimates in mixed-type data. PLoS Computational Biology. 2020;16. https://doi.org/10.1371/journal.pcbi.1007866

25. Vellido A. The importance of interpretability and visualization in machine learning for applications in medicine and health care. Neural Computing and Applications. 2019; 32:18069-18083. https://doi.org/10.1007/s00521-019-04051-w

26. Sifaou H, Kammoun A, Alouini M. High-Dimensional Quadratic Discriminant Analysis Under Spiked Covariance Model. IEEE Access. 2020;8:117313-117323. https://doi.org/10.1109/ACCESS.2020.3004812

27. Ikotun A, Ezugwu E, Abualigah L, Abuhaija B, Heming J. (2023). K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Information Sciences. 2023;622(C):178–210. https://doi.org/10.1016/j.ins.2022.11.139

28. Dembińska A, Jasiński K. Maximum likelihood estimators based on discrete component lifetimes of a k-out-of-n system. TEST. 2020; 30:407-428. https://doi.org/10.1007/s11749-020-00724-0

29. Correia S, Guimarães P, Zylkin T. Fast Poisson estimation with high-dimensional fixed effects. The Stata Journal. 2019; 20:115-95. https://doi.org/10.1177/1536867X20909691

30. Mannam V, Zhang Y, Zhu Y, Nichols E, Wang Q, Sundaresan V, Zhang S, Smith C, Bohn PW, Howard SS. Real-time image denoising of mixed Poisson-Gaussian noise in fluorescence microscopy images using Image. Optica. 2022;9(4):335-345. https://doi.org/10.1364/OPTICA.448287

31. Navya B, Sridevi J, Vasanth K. Modified Geometric Mean as an Estimator of Outlier based Artifacts in Natural Images. 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC). Trichy, India: IEEE; 2022. p. 1095–102. https://doi.org/10.1109/ICOSEC54921.2022.9951924

32. Vera M, Bravo A, Medina R. Description and use of three-dimensional numerical phantoms of cardiac computed tomography images. Data. 2022;7(8):115. https://doi.org/10.3390/data7080115

33. Muñoz Bonilla HA, Menassa Garrido IS, Rojas Coronado L, Espinosa Rodríguez MA. La innovación en el sector servicios y su relación compleja con la supervivencia empresarial. Región Científica. 2024;3(1):2024214. https://doi.org/10.58763/rc2024214

34. Li X, Chen W, Li F, Kang R. Reliability evaluation with limited and censored time-to-failure data based on uncertainty distributions. Applied Mathematical Modelling. 2021;94:403-420. https://doi.org/10.1016/J.APM.2021.01.029

35. Grzegorzewski P, Romaniuk M. Bootstrap Methods for Epistemic Fuzzy Data. International Journal of Applied Mathematics and Computer Science. 2022;32:285-297. https://doi.org/10.34768/amcs-2022-0021

36. Derbyshire J. Answers to questions on uncertainty in geography: Old lessons and new scenario tools. Environment and Planning A: Economy and Space. 2019;52:710-727. https://doi.org/10.1177/0308518X19877885

37. Asim Shahid M, Alam M, Mohd Su'ud M. Improved accuracy and less fault prediction errors via modified sequential minimal optimization algorithm. PloS one. 2023;18(4):e0284209. https://doi.org/10.1371/journal.pone.0284209

38. Schober P, Mascha E, Vetter T. Statistics From A (Agreement) to Z (z Score): A Guide to Interpreting Common Measures of Association, Agreement, Diagnostic Accuracy, Effect Size, Heterogeneity, and Reliability in Medical Research. Anesthesia and analgesia. 2021;133(6):1633-1641. https://doi.org/10.1213/ANE.0000000000005773

39. Sánchez-González J, Rocha-de-Lossada C, Flikier D. Median absolute error and interquartile range as criteria of success against the percentage of eyes within a refractive target in IOL surgery. Journal of Cataract & Refractive Surgery. 2020;46(10):1441. https://doi.org/10.1097/j.jcrs.0000000000000248

40. Chicco D, Warrens MJ, Jurman G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. PeerJ Computer Science. 2021;7:e623. https://doi.org/10.7717/peerj-cs.623

41. Sarker IH. (2021). Data science and analytics: an overview from data-driven smart computing, decision-making and applications perspective. SN Computer Science. 2021;2(5):377. https://doi.org/10.1007/s42979-021-00765-8

42. Antoniano-Villalobos I, Borgonovo E, Lu X. Nonparametric estimation of probabilistic sensitivity measures. Statistics and Computing. 2019; 30:447-467. https://doi.org/10.1007/s11222-019-09887-9

43. Lee M, Khoo M, Chew X, Then P. Effect of Measurement Errors on the Performance of Coefficient of Variation Chart With Short Production Runs. IEEE Access. 2020; 8:72216-72228. https://doi.org/10.1109/ACCESS.2020.2985410

44. Ortiz-Pimiento N, Díaz-Serna F. (2019). Relative average deviation as measure of robustness in the stochastic project scheduling problem. Revista Facultad de Ingeniería. 2019;28(52):77-97. https://doi.org/10.19053/01211129.v28.n52.2019.9756

45. Starczewski J, Goetzen P, Napoli C. Triangular Fuzzy-Rough Set Based Fuzzification of Fuzzy Rule-Based Systems. Journal of Artificial Intelligence and Soft Computing Research. 2020; 10:271-285. https://doi.org/10.2478/jaiscr-2020-0018

46. Shoaip N, El-Sappagh S, Abuhmed T, Elmogy M. A dynamic fuzzy rule-based inference system using fuzzy inference with semantic reasoning. Scientific reports. 2024;14(1):4275. https://doi.org/10.1038/s41598-024-54065-1

47. Eaton JW, Bateman D, Hauberg S, Wehbring R. GNU Octave version 5.1.0 manual: A high-level interactive language for numerical computations. 2019. https://www.gnu.org/software/octave/doc/interpreter

48. Li G, Yang L, Lee C, Wang X, Rong M. A Bayesian Deep Learning RUL Framework Integrating Epistemic and Aleatoric Uncertainties. IEEE Transactions on Industrial Electronics. 2021;68:8829-8841. https://doi.org/10.1109/TIE.2020.3009593

49. Chen S, Zhang Q, Zhang T, Zhang L, Peng L, Wang S. Robust State Estimation With Maximum Correntropy Rotating Geometric Unscented Kalman Filter. IEEE Transactions on Instrumentation and Measurement. 2022;71:1-14. https://doi.org/10.1109/TIM.2021.3137553

50. Cao B, Zhao J, Liu X, Arabas J, Tanveer M, Singh A, Lv Z. Multiobjective Evolution of the Explainable Fuzzy Rough Neural Network With Gene Expression Programming. IEEE Transactions on Fuzzy Systems. 2022;30:4190-4200. https://doi.org/10.1109/TFUZZ.2022.3141761

51. Kubíček J, Strycek M, Cerný M, Penhaker M, Prokop O, Vilimek D. Quantitative and Comparative Analysis of Effectivity and Robustness for Enhanced and Optimized Non-Local Mean Filter Combining Pixel and Patch Information on MR Images of Musculoskeletal System. Sensors. 2021;21. https://doi.org/10.3390/s21124161

52. ARABI H, Zaidi H. Non-local mean denoising using multiple PET reconstructions. Annals of Nuclear Medicine. 2020;35:176-186. https://doi.org/10.1007/s12149-020-01550-y

53. Meng Z, Pang Y, Pu Y, Wang X. New hybrid reliability-based topology optimization method combining fuzzy and probabilistic models for handling epistemic and aleatory uncertainties. Computer Methods in Applied Mechanics and Engineering. 2020;363:112886. https://doi.org/10.1016/j.cma.2020.112886

54. Hüllermeier E, Waegeman W. Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods. Machine Learning. 2019;110:457-506. https://doi.org/10.1007/s10994-021-05946-3

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Published

2024-07-31

How to Cite

1.
Nieto Sánchez ZC, Bravo Valero AJ. Exploring computational methods in the statistical analysis of imprecise medical data: between epistemology and ontology. Salud, Ciencia y Tecnología [Internet]. 2024 Jul. 31 [cited 2024 Dec. 10];4:1341. Available from: https://sct.ageditor.ar/index.php/sct/article/view/660