Chronic Kidney Disease Diagnosis Model Based on Case Based Reasoning
Ermiyas Birihanu Belachew1, Hailemichael Kefie Tamiru2
1Ermiyas Birihanu Belachew, Lecturer, Software Engineering , Wolkite University, Wolkite, Ethiopia
2Hailemichael Kefie Tamiru, Lecturer, Software Engineering , Wolkite University, Wolkite, Ethiopia
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 2738-2743 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B3624129219/2019©BEIESP | DOI: 10.35940/ijeat.B3624.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Provision of health care services is still a major challenge for developing countries. To mention some of the challenges: Lack of highly qualified medical human resources, financial as well as the ability of manage and transform scare resources to meet healthcare needs. In particular, In Ethiopia health care management related to the kidney disorder suffers from the following challenges: lack of highly qualified medical human resources, financial as well as the ability to manage and transform scarce resources to meet healthcare needs.On the one hand, Artificial Intelligence (AI) helps the medical sciences. Hence, in this paper we proposed a framework for CBR system to facilitate and support the diagnosis of chronic kidney diseases with domain expert’s advice. Interview and techniques have been employed on this study to acquire the necessary information required to develop intended CBR system. Finally, we evaluate the performance of the developed framework using recall and precision.
Keywords: Case basereasoning, Preprocessing, framework, Kidney disorder.