Data mining based medical intelligent system for chronic kidney disease diagnosis and treatment in the Oromo language
Keywords:
Afan Oromo medical intelligent, Chronic kidney disease, Machine learning, Medical intelligent systemAbstract
Chronic kidney disease remains a global lethal disease every day, requiring further investigation to tackle the regular heart-breaking death rate admission. In developing countries, the access of a safe infrastructure based on artificial intelligence, the distribution of competent doctors, chronic kidney disease prevention services, and public awareness is severely limited. Medical intelligent systems can address capacity constraint issues in the quest to become the perfect doctor, even exceeding doctors in diagnosis and trappy recommendation. Consequently, the researcher developed a data mining result-based medical intelligent system for chronic kidney disease diagnosis and treatment in Afan Oromo. Hence, the authors used a local dataset gathered using manual and automated knowledge acquisition methods. The preprocessed dataset was modeled and interpreted using different machine learning tools and techniques that converted the resulted rules into a format suitable for the SWI-Prolog tool, command-line software that is primarily used in expert system development. Following that, we have used the SWI-Prolog framework with Java eclipse using Java to prolog connectivity to develop an easy medical intelligent system prototype. The proposed medical intelligent system prototype has been tested for performance and acceptance evaluation and recorded 93.4% and 92.8%, respectively. This is a promising result, proving that the strategy is appealing and useful for diagnosing and treating chronic kidney diseases.
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