A Framework for Early Differential Diagnosis of Tropical Confusable Diseases Using the Fuzzy Cognitive Map Engine
The overarching aim of this study is to develop a soft-computing system for the differential diagnosis of tropical diseases. These conditions are of concern to health bodies, physicians, and the community at large because of their mortality rates, and difficulties in early diagnosis due to the fact that they present with symptoms that overlap, and thus become ‘confusable’. We report on the first phase of our study, which focuses on the development of a fuzzy cognitive map model for early differential diagnosis of tropical diseases. We used malaria as a case disease to show the effectiveness of the FCM technology as an aid to the medical practitioner in the diagnosis of tropical diseases. Our model takes cognizance of manifested symptoms and other non-clinical factors that could contribute to symptoms manifestations. Our model showed 85% accuracy in diagnosis, as against the physicians’ initial hypothesis, which stood at 55% accuracy. It is expected that the next stage of our study will provide a multi-disease, multi-symptom model that also improves efficiency by utilizing a decision support filter that works on an algorithm, which mimics the physician’s diagnosis process.
[1] WHO (World health organization) (2015); World Health Organization, World Health Statistics 2015, World Health Organization, Geneva, Switzerland.
[2] Orimadegun, A., Amodu, O., Olumese, P., and Omotade, O. (2008). Early home treatment of childhood fevers with ineffective antimalarials is deleterious in the outcome of severe malaria. Malaria journal, 7(1), 143.
[3] Thierfelder, C., Schill, C., Hatz, C. and Nuesch, R. (2008). Trends in imported malaria to Besel, Switzerland. Journal of Travel Medicine, 15(6), 432-436.
[4] Podgorelec V. and Kokol P. (2001); Towards more optimal medical diagnosing with evolutionary algorithms. Journal of Medical Systems, 25 (3), 195-219
[5] Szolovits, P; Patil, R.S; and Schwartz W.B (1988); “Artificial Intelligence in Medical Diagnosis”, Annals of Internal Medicine, 108(1):80-87
[6] Driver, C. (2009). Malaria and its avoidance. Practice Nurse, 37(8), 19-24.
[7] Wyatt, J and Spiegelhalter, D. (1991), "Field trials of medical decision-aids: potential problems and solutions", in Clayton, P. (Eds), Proceedings of the Fifteenth Annual Symposium on Computer Applications in Medical Care, American Medical Informatics Association, Washington, DC, pp.3-7.
[8] Seising, R. (2006). From vagueness in medical thought to the foundations of fuzzy reasoning in medical diagnosis. Artificial Intelligence in Medicine, 38, 237-257.
[9] Schlagenhauf, P., Weld, L., Goorhuis, A., Gautret, P., Weber, R., von Sonnenburg, F., ... & Grobusch, M. P. (2015). Travel-associated infection presenting in Europe (2008–12): an analysis of EuroTravNet longitudinal, surveillance data, and evaluation of the effect of the pre-travel consultation. The Lancet Infectious Diseases, 15(1), 55-64
[10] Rotberg, R. I., & Aker, J. C. (2013). Mobile phones: uplifting weak and failed states. The Washington Quarterly, 36(1), 111-125.
[11] Kulikowski, C.A (1987); “Artificial Intelligence in Medicine: A Personal Retrospective on its Emergence and Early Evolution”, ACM (0-89791-248-9/87/0011/0199), pp199-206
[12] Gorry, G.A (1973); “Computer Assisted Clinical Decision Making Methods”, Journal of Medicine, 12: 45-51
[13] Shortliffe, E. H. (1974); “MYCIN: A rule-based computer program for advising physicians regarding antimicrobial therapy selection”, Proceedings of the ACM National Congress (SIGBIO Session), p. 739.
[14] Szolovits, P (1995); “Uncertainty and Decisions in Medical Informatics”; Methods of Information in Medicine, 34:111-121
[15] Kaeding, A.K and Flor T. (1995); “Processing Unexact Information in a Medical Used Multiparadigm System”, Proceedings of the 1995 ACM symposium on Applied computing 1995, Nashville, Tennessee, United States February 26 - 28, 1995, pp590-592
[16] Song, Q., Kasabov, N (2003)., "A Novel Generic Higher-order TSK Fuzzy Model for Prediction and Applications for Medical Decision Support”, Proc. of The Eighth Australian and New Zealand Intelligence Information Systems Conference (ANZIIS2003), Sydney, Australia, 10 – 12, December, 2003, pp. 241 – 245.
[17] Lisboa, P. J., and Taktak, A. F. (2006). The use of artificial neural networks in decision support in cancer: a systematic review. Neural networks, 19(4), 408-415.
[18] Polat, K., and Güneş, S. (2007). A hybrid approach to medical decision support systems: Combining feature selection, fuzzy weighted pre-processing and AIRS. computer methods and programs in biomedicine, 88(2), 164-174.
[19] Obot O. and Uzoka F.M.E (2009); A Framework for Application of Neuro-Case-Rule base Hybridization in Medical Diagnosis Applied Soft Computing Journal, 9: 245-253
[20] Barca, C. C., Rodríguez, J. M., Puddu, P. E., Luštrek, M., Cvetković, B., Bordone, M., ... and Tamburini, E. (2014, January). Advanced Medical Expert Support Tool (A-MEST): EHR-Based Integration of Multiple Risk Assessment Solutions for Congestive Heart Failure Patients. In XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013 (pp. 1334-1337). Springer International Publishing.
[21] Kosko B. (1986); Fuzzy Cognitive Maps. International Journal of Man-Machine Studies 24, 65–75
[22] Zadeh, L. A (1965).: “Fuzzy Sets and Systems”; In: Fox, J. (ed.): Proceedings Symposium on System Theory, Polytechnic Institute of Brooklyn, April 1965, pp. 29-37.
[23] Tolman E.C. (1948); Cognitive maps in rats and men. Psychological Review, 55 (4): 189–208.
[24] Georgopoulos, V. C., Malandraki, G. A., and Stylios, C. D. (2003). A fuzzy cognitive map approach to differential diagnosis of specific language impairment. Artificial intelligence in Medicine, 29(3), 261-278.
[25] Papageorgiou, E. I., Papandrianos, N. I., Karagianni, G., Kyriazopoulos, G. C., and Sfyras, D. (2009, August). A fuzzy cognitive map based tool for prediction of infectious diseases. In Fuzzy Systems, 2009. FUZZ-IEEE 2009. IEEE International Conference on (pp. 2094-2099). IEEE.
[26] John, R. I., and Innocent, P. R. (2005). Modeling uncertainty in clinical diagnosis using fuzzy logic. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 35(6), 1340-1350.
[27] Reggia, J. A., Nau, D. S., and Wang, P. Y. (1983). Diagnostic expert systems based on a set covering model. International Journal of Man-Machine Studies, 19(5), 437-460.
[28] Miller, G.A(1956); “The Magical Number even, Plus or Minus Two: Some Limits on Our Capacity for Processing Information”, Psychological Review, 63: 81-97
[29] Papageorgiou, E. I., Papandrianos, N. I., Apostolopoulos, D. J., and Vassilakos, P. J. (2008, June). Fuzzy cognitive map based decision support system for thyroid diagnosis management. In Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on (pp. 1204-1211). IEEE.
[30] Lopes, M. H. B. D. M., Ortega, N. R. S., Silveira, P. S. P., Massad, E., Higa, R., and Marin, H. D. F. (2013). Fuzzy cognitive map in differential diagnosis of alterations in urinary elimination: A nursing approach. International journal of medical informatics, 82(3), 201-208.
[31] Mago, V. K., Papageorgiou, E. I., and Mago, A. (2014). Employing Fuzzy Cognitive Map for Periodontal Disease Assessment. In Fuzzy Cognitive Maps for Applied Sciences and Engineering (pp. 375-389). Springer Berlin Heidelberg.
[32] Marateb, H. R., Mansourian, M., Faghihimani, E., Amini, M., and Farina, D. (2014). A hybrid intelligent system for diagnosing microalbuminuria in type 2 diabetes patients without having to measure urinary albumin. Computers in Biology and Medicine, 45, 34-42.
[33] Khan, M. S., Chong, A., and Quaddus, M. (1987). Fuzzy cognitive maps and intelligent decision support–a review. School of Information Technology, Murdoch University, Graduate School of Business, Curtin University of Technology, GPO Box U. Downloaded on December 10, 2013 from http://citeseerx.ist.psu.edu/viewdoc/download? doi=10.1.1.127.7815&rep=rep1&type=pdf
[34] Khan, M. S., and Quaddus, M. (2004). Group decision support using fuzzy cognitive maps for causal reasoning. Group Decision and Negotiation, 13(5), 463-480.p
[35] Taber, R. (1991). Knowledge processing with fuzzy cognitive maps. Expert Systems with Applications, 2(1), 83-87.
[36] Carvalho J.P. and Tomé J.A. (1999); Rule Based Fuzzy Cognitive Maps and Fuzzy Cognitive Maps - A Comparative Study, Proceedings of the 18th International Conference of the North American Fuzzy Information Processing Society, NAFIPS99, New York. Accessed on July 5, 2012 from http://www.inesc-id.pt/pt/indicadores/Ficheiros/1037.pdf
[37] Uzoka, F.M.E., and Barker, K. (2010). Expert Systems and Uncertainty in Medical Diagnosis: A Proposal for Fuzzy-ANP Hybridization. International Journal of Medical Engineering and Informatics, 2(2), 329-342.
[38] Ermine, J.L (1995); Expert System: Theory and Practice, New Dehli, Prentice Hall of India Private Limited.
[39] Obot, O. U., Uzoka, F. M., Akinyokun, O. C., and Andy, J. J. (2013). Conventional and neuro-fuzzy framework for diagnosis and therapy of cardiovascular disease. Bio-Algorithms and Med-Systems, 9(3), 115-133.
[40] Reibnegger, G., Fuchs, D., Hausen, A., Schmutzhard, E., Werner, E. R., & Wachter, H. (1987). The dependence of cell-mediated immune activation in malaria on age and endemicity. Transactions of the Royal Society of Tropical Medicine and Hygiene, 81(5), 729-729.
[41] Dondorp, A. M., Lee, S. J., Faiz, M. A., Mishra, S., Price, R., Tjitra, E., ... & White, N. J. (2008). The relationship between age and the manifestations of and mortality associated with severe malaria. Clinical Infectious Diseases, 47(2), 151-157.
[42] Schwartz E, Sedetzki S, Murad H, and Raveh D (2001); Age as a risk factor for severe plasmodium falciparum malaria in non-immune patients. Clinical Infectious Diseases, 33(10): 1774-1777
[43] Doherty, J. F., Grant, A. D., & Bryceson, A. D. M. (1995). Fever as the presenting complaint of travellers returning from the tropics. Qjm, 88(4), 277-281.
[44] Cantrill, M. D., (2010). Computers in patient care: the promise and the challenge. Communications of the ACM, 53(9): 42-47.
[45] Grove, W. M., Zald, D. H., Lebow, B. S., Snitz, B. E., & Nelson, C. (2000). Clinical versus mechanical prediction: a meta-analysis. Psychological assessment, 12(1), 19-30.
[46] Bright, T. J., Wong, A., Dhurjati, R., Bristow, E., Bastian, L., Coeytaux, R. R., ... & Lobach, D. (2012). Effect of clinical decision-support systemsa systematic review. Annals of internal medicine, 157(1), 29-43.
[1] WHO (World health organization) (2015); World Health Organization, World Health Statistics 2015, World Health Organization, Geneva, Switzerland.
[2] Orimadegun, A., Amodu, O., Olumese, P., and Omotade, O. (2008). Early home treatment of childhood fevers with ineffective antimalarials is deleterious in the outcome of severe malaria. Malaria journal, 7(1), 143.
[3] Thierfelder, C., Schill, C., Hatz, C. and Nuesch, R. (2008). Trends in imported malaria to Besel, Switzerland. Journal of Travel Medicine, 15(6), 432-436.
[4] Podgorelec V. and Kokol P. (2001); Towards more optimal medical diagnosing with evolutionary algorithms. Journal of Medical Systems, 25 (3), 195-219
[5] Szolovits, P; Patil, R.S; and Schwartz W.B (1988); “Artificial Intelligence in Medical Diagnosis”, Annals of Internal Medicine, 108(1):80-87
[6] Driver, C. (2009). Malaria and its avoidance. Practice Nurse, 37(8), 19-24.
[7] Wyatt, J and Spiegelhalter, D. (1991), "Field trials of medical decision-aids: potential problems and solutions", in Clayton, P. (Eds), Proceedings of the Fifteenth Annual Symposium on Computer Applications in Medical Care, American Medical Informatics Association, Washington, DC, pp.3-7.
[8] Seising, R. (2006). From vagueness in medical thought to the foundations of fuzzy reasoning in medical diagnosis. Artificial Intelligence in Medicine, 38, 237-257.
[9] Schlagenhauf, P., Weld, L., Goorhuis, A., Gautret, P., Weber, R., von Sonnenburg, F., ... & Grobusch, M. P. (2015). Travel-associated infection presenting in Europe (2008–12): an analysis of EuroTravNet longitudinal, surveillance data, and evaluation of the effect of the pre-travel consultation. The Lancet Infectious Diseases, 15(1), 55-64
[10] Rotberg, R. I., & Aker, J. C. (2013). Mobile phones: uplifting weak and failed states. The Washington Quarterly, 36(1), 111-125.
[11] Kulikowski, C.A (1987); “Artificial Intelligence in Medicine: A Personal Retrospective on its Emergence and Early Evolution”, ACM (0-89791-248-9/87/0011/0199), pp199-206
[12] Gorry, G.A (1973); “Computer Assisted Clinical Decision Making Methods”, Journal of Medicine, 12: 45-51
[13] Shortliffe, E. H. (1974); “MYCIN: A rule-based computer program for advising physicians regarding antimicrobial therapy selection”, Proceedings of the ACM National Congress (SIGBIO Session), p. 739.
[14] Szolovits, P (1995); “Uncertainty and Decisions in Medical Informatics”; Methods of Information in Medicine, 34:111-121
[15] Kaeding, A.K and Flor T. (1995); “Processing Unexact Information in a Medical Used Multiparadigm System”, Proceedings of the 1995 ACM symposium on Applied computing 1995, Nashville, Tennessee, United States February 26 - 28, 1995, pp590-592
[16] Song, Q., Kasabov, N (2003)., "A Novel Generic Higher-order TSK Fuzzy Model for Prediction and Applications for Medical Decision Support”, Proc. of The Eighth Australian and New Zealand Intelligence Information Systems Conference (ANZIIS2003), Sydney, Australia, 10 – 12, December, 2003, pp. 241 – 245.
[17] Lisboa, P. J., and Taktak, A. F. (2006). The use of artificial neural networks in decision support in cancer: a systematic review. Neural networks, 19(4), 408-415.
[18] Polat, K., and Güneş, S. (2007). A hybrid approach to medical decision support systems: Combining feature selection, fuzzy weighted pre-processing and AIRS. computer methods and programs in biomedicine, 88(2), 164-174.
[19] Obot O. and Uzoka F.M.E (2009); A Framework for Application of Neuro-Case-Rule base Hybridization in Medical Diagnosis Applied Soft Computing Journal, 9: 245-253
[20] Barca, C. C., Rodríguez, J. M., Puddu, P. E., Luštrek, M., Cvetković, B., Bordone, M., ... and Tamburini, E. (2014, January). Advanced Medical Expert Support Tool (A-MEST): EHR-Based Integration of Multiple Risk Assessment Solutions for Congestive Heart Failure Patients. In XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013 (pp. 1334-1337). Springer International Publishing.
[21] Kosko B. (1986); Fuzzy Cognitive Maps. International Journal of Man-Machine Studies 24, 65–75
[22] Zadeh, L. A (1965).: “Fuzzy Sets and Systems”; In: Fox, J. (ed.): Proceedings Symposium on System Theory, Polytechnic Institute of Brooklyn, April 1965, pp. 29-37.
[23] Tolman E.C. (1948); Cognitive maps in rats and men. Psychological Review, 55 (4): 189–208.
[24] Georgopoulos, V. C., Malandraki, G. A., and Stylios, C. D. (2003). A fuzzy cognitive map approach to differential diagnosis of specific language impairment. Artificial intelligence in Medicine, 29(3), 261-278.
[25] Papageorgiou, E. I., Papandrianos, N. I., Karagianni, G., Kyriazopoulos, G. C., and Sfyras, D. (2009, August). A fuzzy cognitive map based tool for prediction of infectious diseases. In Fuzzy Systems, 2009. FUZZ-IEEE 2009. IEEE International Conference on (pp. 2094-2099). IEEE.
[26] John, R. I., and Innocent, P. R. (2005). Modeling uncertainty in clinical diagnosis using fuzzy logic. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 35(6), 1340-1350.
[27] Reggia, J. A., Nau, D. S., and Wang, P. Y. (1983). Diagnostic expert systems based on a set covering model. International Journal of Man-Machine Studies, 19(5), 437-460.
[28] Miller, G.A(1956); “The Magical Number even, Plus or Minus Two: Some Limits on Our Capacity for Processing Information”, Psychological Review, 63: 81-97
[29] Papageorgiou, E. I., Papandrianos, N. I., Apostolopoulos, D. J., and Vassilakos, P. J. (2008, June). Fuzzy cognitive map based decision support system for thyroid diagnosis management. In Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on (pp. 1204-1211). IEEE.
[30] Lopes, M. H. B. D. M., Ortega, N. R. S., Silveira, P. S. P., Massad, E., Higa, R., and Marin, H. D. F. (2013). Fuzzy cognitive map in differential diagnosis of alterations in urinary elimination: A nursing approach. International journal of medical informatics, 82(3), 201-208.
[31] Mago, V. K., Papageorgiou, E. I., and Mago, A. (2014). Employing Fuzzy Cognitive Map for Periodontal Disease Assessment. In Fuzzy Cognitive Maps for Applied Sciences and Engineering (pp. 375-389). Springer Berlin Heidelberg.
[32] Marateb, H. R., Mansourian, M., Faghihimani, E., Amini, M., and Farina, D. (2014). A hybrid intelligent system for diagnosing microalbuminuria in type 2 diabetes patients without having to measure urinary albumin. Computers in Biology and Medicine, 45, 34-42.
[33] Khan, M. S., Chong, A., and Quaddus, M. (1987). Fuzzy cognitive maps and intelligent decision support–a review. School of Information Technology, Murdoch University, Graduate School of Business, Curtin University of Technology, GPO Box U. Downloaded on December 10, 2013 from http://citeseerx.ist.psu.edu/viewdoc/download? doi=10.1.1.127.7815&rep=rep1&type=pdf
[34] Khan, M. S., and Quaddus, M. (2004). Group decision support using fuzzy cognitive maps for causal reasoning. Group Decision and Negotiation, 13(5), 463-480.p
[35] Taber, R. (1991). Knowledge processing with fuzzy cognitive maps. Expert Systems with Applications, 2(1), 83-87.
[36] Carvalho J.P. and Tomé J.A. (1999); Rule Based Fuzzy Cognitive Maps and Fuzzy Cognitive Maps - A Comparative Study, Proceedings of the 18th International Conference of the North American Fuzzy Information Processing Society, NAFIPS99, New York. Accessed on July 5, 2012 from http://www.inesc-id.pt/pt/indicadores/Ficheiros/1037.pdf
[37] Uzoka, F.M.E., and Barker, K. (2010). Expert Systems and Uncertainty in Medical Diagnosis: A Proposal for Fuzzy-ANP Hybridization. International Journal of Medical Engineering and Informatics, 2(2), 329-342.
[38] Ermine, J.L (1995); Expert System: Theory and Practice, New Dehli, Prentice Hall of India Private Limited.
[39] Obot, O. U., Uzoka, F. M., Akinyokun, O. C., and Andy, J. J. (2013). Conventional and neuro-fuzzy framework for diagnosis and therapy of cardiovascular disease. Bio-Algorithms and Med-Systems, 9(3), 115-133.
[40] Reibnegger, G., Fuchs, D., Hausen, A., Schmutzhard, E., Werner, E. R., & Wachter, H. (1987). The dependence of cell-mediated immune activation in malaria on age and endemicity. Transactions of the Royal Society of Tropical Medicine and Hygiene, 81(5), 729-729.
[41] Dondorp, A. M., Lee, S. J., Faiz, M. A., Mishra, S., Price, R., Tjitra, E., ... & White, N. J. (2008). The relationship between age and the manifestations of and mortality associated with severe malaria. Clinical Infectious Diseases, 47(2), 151-157.
[42] Schwartz E, Sedetzki S, Murad H, and Raveh D (2001); Age as a risk factor for severe plasmodium falciparum malaria in non-immune patients. Clinical Infectious Diseases, 33(10): 1774-1777
[43] Doherty, J. F., Grant, A. D., & Bryceson, A. D. M. (1995). Fever as the presenting complaint of travellers returning from the tropics. Qjm, 88(4), 277-281.
[44] Cantrill, M. D., (2010). Computers in patient care: the promise and the challenge. Communications of the ACM, 53(9): 42-47.
[45] Grove, W. M., Zald, D. H., Lebow, B. S., Snitz, B. E., & Nelson, C. (2000). Clinical versus mechanical prediction: a meta-analysis. Psychological assessment, 12(1), 19-30.
[46] Bright, T. J., Wong, A., Dhurjati, R., Bristow, E., Bastian, L., Coeytaux, R. R., ... & Lobach, D. (2012). Effect of clinical decision-support systemsa systematic review. Annals of internal medicine, 157(1), 29-43.
@article{"International Journal of Information, Control and Computer Sciences:72265", author = "Faith-Michael E. Uzoka and Boluwaji A. Akinnuwesi and Taiwo Amoo and Flora Aladi and Stephen Fashoto and Moses Olaniyan and Joseph Osuji", title = "A Framework for Early Differential Diagnosis of Tropical Confusable Diseases Using the Fuzzy Cognitive Map Engine", abstract = "The overarching aim of this study is to develop a soft-computing system for the differential diagnosis of tropical diseases. These conditions are of concern to health bodies, physicians, and the community at large because of their mortality rates, and difficulties in early diagnosis due to the fact that they present with symptoms that overlap, and thus become ‘confusable’. We report on the first phase of our study, which focuses on the development of a fuzzy cognitive map model for early differential diagnosis of tropical diseases. We used malaria as a case disease to show the effectiveness of the FCM technology as an aid to the medical practitioner in the diagnosis of tropical diseases. Our model takes cognizance of manifested symptoms and other non-clinical factors that could contribute to symptoms manifestations. Our model showed 85% accuracy in diagnosis, as against the physicians’ initial hypothesis, which stood at 55% accuracy. It is expected that the next stage of our study will provide a multi-disease, multi-symptom model that also improves efficiency by utilizing a decision support filter that works on an algorithm, which mimics the physician’s diagnosis process.
", keywords = "Medical diagnosis, tropical diseases, fuzzy cognitive map, decision support filters, malaria differential diagnosis.", volume = "10", number = "2", pages = "346-8", }
