Research article Special Issues

A Clinical Decision Support System based on fuzzy rules and classification algorithms for monitoring the physiological parameters of type-2 diabetic patients

  • Received: 30 December 2020 Accepted: 04 March 2021 Published: 19 March 2021
  • The use of different types of Clinical Decision Support Systems (CDSS) makes possible the improvement of the quality of the therapeutic and diagnostic efficiency in health field. Those systems, properly implemented, are able to simulate human expert clinician reasoning in order to suggest decisions on treatment of patients. In this paper, we exploit fuzzy inference machines to improve the quality of the day-by-day clinical care of type-2 diabetic patients of Anti-Diabetes Centre (CAD) of the Local Health Authority ASL Naples 1 (Naples, Italy). All the designed functionalities were developed thanks to the experience on the field, through different phases (data collection and adjustment, Fuzzy Inference System development and its validation on real cases) executed by an interdisciplinary research team comprising doctors, clinicians and IT engineers. The proposed approach also allows the remote monitoring of patients' clinical conditions and, hence, can help to reduce hospitalizations.

    Citation: Colella Ylenia, De Lauri Chiara, Improta Giovanni, Rossano Lucia, Vecchione Donatella, Spinosa Tiziana, Giordano Vincenzo, Verdoliva Ciro, Santini Stefania. A Clinical Decision Support System based on fuzzy rules and classification algorithms for monitoring the physiological parameters of type-2 diabetic patients[J]. Mathematical Biosciences and Engineering, 2021, 18(3): 2654-2674. doi: 10.3934/mbe.2021135

    Related Papers:

  • The use of different types of Clinical Decision Support Systems (CDSS) makes possible the improvement of the quality of the therapeutic and diagnostic efficiency in health field. Those systems, properly implemented, are able to simulate human expert clinician reasoning in order to suggest decisions on treatment of patients. In this paper, we exploit fuzzy inference machines to improve the quality of the day-by-day clinical care of type-2 diabetic patients of Anti-Diabetes Centre (CAD) of the Local Health Authority ASL Naples 1 (Naples, Italy). All the designed functionalities were developed thanks to the experience on the field, through different phases (data collection and adjustment, Fuzzy Inference System development and its validation on real cases) executed by an interdisciplinary research team comprising doctors, clinicians and IT engineers. The proposed approach also allows the remote monitoring of patients' clinical conditions and, hence, can help to reduce hospitalizations.



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    [1] E. Bonora, S. Cataudella, G. Marchesini, R. Miccoli, O. Vaccaro, G. P. Fadini, Clinical burden of diabetes in Italy in 2018: a look at a systemic disease from the ARNO Diabetes Observatory, BMJ Open Diabetes Res. Care, 8 (2020), e001191.
    [2] T. Klingeberg, M. Schilling, Mobile wearable device for long term monitoring of vital signs, Comput. Methods Prog. Biomed., 106 (2012), 89–96. doi: 10.1016/j.cmpb.2011.12.009
    [3] G. Improta, M. Triassi, G. Guizzi, L. C. Santillo, R. Revetria, A. Catania, et al., An innovative contribution to health technology assessment, in Modern Advances in Intelligent Systems and Tools, Springer, (2012), 127–131.
    [4] C. F. Lin, Mobile telemedicine: A survey study, J. Med. Syst., 36 (2012), 511–520. doi: 10.1007/s10916-010-9496-x
    [5] F. M. E. Uzoka, O. Obot, K. Barker, J. Osuji, An experimental comparison of fuzzy logic and analytic hierarchy process for medical decision support systems, Comput. Methods Prog. Biomed., 103 (2011), 10–27. doi: 10.1016/j.cmpb.2010.06.003
    [6] I. Graham, P. L. Jones, Expert Systems: Knowledge, Uncertainty, and Decision, Chapman and Hall, 1998.
    [7] C. Habib, A. Makhoul, R. Darazi, R. Couturier, Multisensor data fusion and decision support in wireless body sensor networks., in NOMS 2016-2016 IEEE/IFIP Network Operations and Management Symposium, (2016), 708–712.
    [8] N. Barakat, A. P. Bradley, M. N. H. Barakat, Intelligible support vector machines for diagnosis of diabetes mellitus, IEEE Trans. Inf. Technol. Biomed., 14 (2010), 1114–1120. doi: 10.1109/TITB.2009.2039485
    [9] J. P. Kandhasamy, S. Balamurali, Performance analysis of classifier models to predict diabetes mellitus, Procedia Comput. Sci., 47 (2015), 45–51. doi: 10.1016/j.procs.2015.03.182
    [10] A. Charleonnan, T. Fufaung, T. Niyomwong, W. Chokchueypattanakit, S. Suwannawach, N. Ninchawee, Predictive Analytics for Chronic Kidney Disease Using Machine Learning Techniques, in 2016 Management and Innovation Technology International Conference (MITicon), (2016).
    [11] S. Dwivedi, R. Borse, A. Yametkar, Lung cancer detection and classification by using machine learning & multinomial Bayesian, IOSR J. Electron. Commun. Eng., 9 (2014), 69–75. doi: 10.9790/2834-09349097
    [12] S. Santini; A. Pescapé; A. S. Valente; V. Abate; G. Improta, M. Triassi, et al., Using fuzzy logic for improving clinical daily-care of β-thalassemia patients, in 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, (2017), 5090–6034.
    [13] G. Improta, V. Mazzella, D. Vecchione, S. Santini, M. Triassi, Fuzzy logic–based clinical decision support system for the evaluation of renal function in post-Transplant Patients, J. Eval. Clin. Pract., 26 (2020), 1224–1234. doi: 10.1111/jep.13302
    [14] Novruz Allahverdi, S. Torun, I. Saritas, Design Of A Fuzzy Expert System For Determination Of Coronary Heart Disease Risk, in International Conference on Computer Systems and Technologies, (2007).
    [15] C. R. M. Leite, G. R. Sizilio, A. D. Neto, R. A Valentim, A. M. Guerreiro, A fuzzy model for processing and monitoring vital signs in ICU patients, Biomed. Eng. Online, 10 (2011), 68. doi: 10.1186/1475-925X-10-68
    [16] G. Cappon, M. Vettoretti, F. Marturano, A. Facchinetti, G. Sparacino, A neural-network-based approach to personalize insulin bolus calculation using continuous glucose monitoring, J. Diabetes Sci. Technol., 12 (2018), 265–272. doi: 10.1177/1932296818759558
    [17] W. C. Hsu, K. H. K. Lau, R. Huang, S. Ghiloni, H. Le, S. Gilroy, et al., Utilization of a cloud-based diabetes management program for insulin initiation and titration enables collaborative decision making between healthcare providers and patients, Diabetes Technol. Ther., 18 (2016), 59–67. doi: 10.1089/dia.2015.0160
    [18] P. Chen, C. Pan, Diabetes classification model based on boosting algorithms, BMC Bioinf., 19 (2018), 109. doi: 10.1186/s12859-018-2090-9
    [19] B. J. Lee, J. Y. Kim, Identification of type 2 diabetes risk factors using phenotypes consisting of anthropometry and triglycerides based on machine learning, IEEE J. Biomed. Health Inf., 20 (2016), 39–46. doi: 10.1109/JBHI.2015.2396520
    [20] P. Grant, A new approach to diabetic control: fuzzy logic and insulin pump technology, Med. Eng. Phys., 29 (2007), 824–827. doi: 10.1016/j.medengphy.2006.08.014
    [21] B. Cosenza, Off-line control of the postprandial glycemia in type 1 diabetes patients by a fuzzy logic decision support, Exp. Syst. Appl., 39 (2012), 10693–10699. doi: 10.1016/j.eswa.2012.02.198
    [22] L. A. Zadeh, The role of fuzzy logic in modeling, identification and control, in Fuzzy Sets, Fuzzy Logic, And Fuzzy Systems, (1996), 783–795.
    [23] C. C. Lee, Fuzzy logic in control systems: fuzzy logic controller. I, IEEE Trans. Syst. Man Cybern., 20 (1990), 404–418.
    [24] F. Baig, M. S. Khan, Y. Noor, M. Imran, Design model of fuzzy logic medical diagnosis control system, Int. J. Comput. Sci. Eng., 3 (2011), 2093–2108.
    [25] D. Driankov, H. Hellendoorn, M. Reinfrank, An Introduction to Fuzzy Control, Springer Science & Business Media, 2013.
    [26] L. A. Zadeh, The concept of a linguistic variable and its application to approximate reasoning, Inf. Sci., 8 (1975), 199–249. doi: 10.1016/0020-0255(75)90036-5
    [27] E. H. Mamdani, Advances in the linguistic synthesis of fuzzy controllers, Int. J. Man Mach. Stud., 8 (1976), 669–678. doi: 10.1016/S0020-7373(76)80028-4
    [28] M. K. Choudhury, N. Baruah, A fuzzy logic-based expert system for determination of health risk level of patient, Int. J. Res. Eng. Technol., 4 (2015), 261–267.
    [29] M. A. Ameen, J. Liu, K. Kwak, Security and privacy issues in wireless sensor networks for healthcare applications, J. Med. Syst., 36 (2012), 93–101. doi: 10.1007/s10916-010-9449-4
    [30] L. I. Kuncheva, J. J. Rodríguez, A weighted voting framework for classifiers ensembles, Knowl. Inf. Syst., 38 (2014), 259–275.
    [31] P. P. Wang, D. Ruan, E. E. Kerre, Fuzzy logic: A spectrum of theoretical & practical issues, Springer, 2007.
    [32] W. J. Wang, H. R. Lin, Fuzzy control design for the trajectory tracking on uncertain nonlinear systems, IEEE Trans. Fuzzy Syst., 7 (1999), 53–62. doi: 10.1109/91.746308
    [33] D. R. Keshwani, D. D. Jones, G. E. Meyer, R. M. Brand, Rule-based Mamdani-type fuzzy modeling of skin permeability, Appl. Soft Comput., 8 (2008), 285–294. doi: 10.1016/j.asoc.2007.01.007
    [34] S. Russel, P. Norvig, Artificial Intelligence: A Modern Approach, London: Pearson Education Limited, 2013.
    [35] D. H. Choi, B. S. Ahn, S. H. Kim, Multicriteria group decision making under incomplete preference judgments: using fuzzy logic with a linguistic quantifier, Int. J. Intell. Syst., 22 (2007), 641–660. doi: 10.1002/int.20218
    [36] E. H. Mamdani, S. Assilian, An experiment in linguistic synthesis with a fuzzy logic controller, Int. J. Man Mach. Stud., 7 (1975), 1–13. doi: 10.1016/S0020-7373(75)80002-2
    [37] C. P. Subbe, M. Kruger, P. Rutheford, L. Gemmel, Validation of a modified Early Warning Score in medical admissions, QJM, 94 (2001), 521–526. doi: 10.1093/qjmed/94.10.521
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