Abstract
Acute abdomen is one of the emergency diseases which is difficult for follow-up treatment. In developing countries, the mortality and morbidity rate are increasing due to misdiagnosis, delay, lack of knowledge and shortage of skilled manpower. These factors affect the quality of health care service in hospitals and reduce the quality of decisions made by physicians. This research attempts to investigate the applicability KBDSS using integration of rule based and case-based reasoning approach so as to improve the quality of decision made by domain experts, to provide effective and efficient services to the patients and to improve shortage of human expert in specific domain area. Domain knowledge is acquired using semi-structured interview technique. Domain experts are selected from Felege hiwot referral hospital in Bahir-Dar. In addition, secondary data is acquired from different sources following DSR methodology. The conceptual model of the knowledge-based system used a decision tree structure which is easy to understand and interpret the procedures involved in patient diagnoses. Based on the conceptual model, the prototype is developed with SWI prolog and java software tools. Beyond the domain expert, the result shows the system has accuracy of 99% for acute abdominal cause classification, and 66% for severity level identification. Whereas, the accuracy value of physicians has 84% for acute abdominal cause classification, and 50% for severity level identification with regard to the final result. Therefore, the KBDSS can diagnose patients with highest accuracy. We were taking the final observed value as pivoting point to test the KBDSS. In the evaluation of the system that classified the stored attribute according to the target problem is 71.33% accuracy. As the result shows, the three reasoning approaches: hybrid, case-based, and rule-based has an accuracy of 87.66%, 70%, and 60% respectively to retrieve target attributes for the target problem. This shows that hybrid reasoning approach is recommended to health care decision support system. Automation of KBDSS has a high contribution to establish truthful decision-making process in patient’s acute abdomen treatment.
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Adamu, A., Maigatari, M., Lawal, K., Iliyasu, M.: Waiting time for emergency abdominal surgery in Zaria, Nigeria. Afr. Health Sci. 10(1), 46–53 (2010). https://www.researchgate.net/publication/46111242
Addisu, M., Tadess, A., Eyobe, T.: Pattern of acute abdomen in Dil Chora referral hospital, Eastern Ethiopia. Int. J. Collab. Res. Internal Med. Publ. Health 8(11), 607–615 (2016)
Ashley, H., Bennet, B., Marie, C.: The evaluation of the acute abdomen. In: Moore, L., Todd, S. (eds.) Common Problems in Acute Care Surgery, pp. 19–31. Springer, New York (2013). https://doi.org/10.1007/978-1-4614-6123-4_2
Fei, J., et al.: Artificial intelligence in healthcare: past, present and future. Stroke Vasc. 2, 1–14 (2017)
Hamid, M.: Design science research as an approach to develop conceptual solutions for improving cost management in construction (2014)
Jay, W.M., Charles, P.D., Anand, B.S.: Abdominal Pain (causes, remedies, treatment). (MedicineNet.com) (2017). https://www.medicinenet.com/abdominal_pain_causes_remedies_treatment/article.htm. Accessed 24 Nov 2017
Jim, P., Ioannis, H.: Integration of rule-based and case based reasoning. Hybrid Intell. Syst. 6(4), 189–209 (2009)
Karde, E., Stefan, J.: Prolog Programming in Artificial Intelligence. Addison-Wesley Publishing Company, Boston (1986)
Ken, P., Tuure, T.: A design science research methodology for information system researches. J. Manag. Inf. Syst. 24, 45–77 (2007)
Leondes, C.T.: Intelligent Knowledge Based Systems. University of California, Los Angeles, Kluwer Academic Publishers, Los Angeles (2005)
Nyundo, M., Rugwizangoga, E., NtakiyirutaI, G., Kakande, I.: Outcome of emergency abdominal surgery at Kigali university teaching hospital: a review of 229 cases. East Cent. Afr. J. Surg. 18(1), 31–39 (2013). Retrieved 27 Nov 2017
Prasath, V., Lakshmi, N., Nathiya, M., Bharathan, N., Neetha, P.: A survey on the applications of fuzzy logic in medical diagnosis. Int. J. Sci. Eng. Res. 4(4), 1199–1203 (2013). http://www.ijser.org
Ray, S., Patel, M., Parmar, H.: Management of acute abdomen: study of 110 cases, 3(2), 18–24 (2016). http://iaimjournal.com/. Accessed 24 Nov 2017
Tejinder, S., Jhunjhunu, R.: New software development methodology for student of Java programming language. Int. J. Comput. Commun. Eng. 2(2), 194–196 (2013)
Thomas, M., Fabrizio, C., Shantale, C.: Decision support systems in medicine - anesthesia, critical care and intensive care medicine. McGill University, Intact (2012)
Zaraté, P., Shaofeng, L.: A new trend for knowledge-based decision support system design. Int. J. Inf. Decis. Sci. 8(3), 305–324 (2016)
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Workneh, A., Teferi, D., Kumilachew, A. (2019). Knowledge Based Decision Support System for Detecting and Diagnosis of Acute Abdomen Using Hybrid Approach. In: Mekuria, F., Nigussie, E., Tegegne, T. (eds) Information and Communication Technology for Development for Africa. ICT4DA 2019. Communications in Computer and Information Science, vol 1026. Springer, Cham. https://doi.org/10.1007/978-3-030-26630-1_6
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DOI: https://doi.org/10.1007/978-3-030-26630-1_6
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