The Implementation of Data Mining with Knowledge Based System for Diagnosis and Treatment of Cattle Diseases: The case of International Livestock Research Institute (ILRI) Animal Health Calabar

The Implementation of Data Mining with Knowledge-Based System for Diagnosis and Treatment of Cattle Diseases: The Case of International Livestock Research Institute (ILRI) Animal Health Calabar

Nigeria is one of the nations that possess the largest livestock population in the African continent with an estimated 56 Million cattle, 58 Million sheep and goats and 10 Million equines, 1 Million camels, and 57 Million chickens. Ethiopia has great potential for increasing livestock production, both for local use and export. However, development has been constrained by numerous reasons. In this study, the possibility of integrating data mining results with a knowledge-based system is realized and explored. The integration process began by taking samples of the ILRC dataset. The dataset is preprocessed and made suitable for mining steps. Due to several limitations in acquiring knowledge for knowledge base from domain experts in the area of diagnosis and treatment of cattle disease, integrated (manual and automated) knowledge acquisition techniques were used to acquire knowledge. Data mining has proven to induce hidden knowledge from large collections of datasets. Hence, the data mining classifier, JRip is employed for the knowledge acquisition step since it has performed best among the selected classifiers with an accuracy of 97.68%. To identify the best prediction model for the diagnosis and treatment of cattle disease, 6 experiments for three classification algorithms, namely J48 pruned, Naïve Bayes, and JRip under a ten-fold Cross- Validation test option and percentage split test option were conducted. Finally, by conducting objective and subjective interestingness measures, the researcher decided to use rules that are generated by the JRip classification algorithm model for further use in the development of the knowledge base system because it registered better performance than J48 and Naïve Bayes with 97.68%, 96.65%, and 95.42% evaluation result in 10-fold cross-validation respectively. The prototype Knowledge-Based System, which provides advice for Animal Health Workers about diagnosis and treatment of cattle disease was developed using SWI-Prolog 7.7.13 with NetBeans 8.2. The proposed Knowledge-Based System has a Knowledge Base, Inference Engines, Explanation Facility, and User Interface. Then 70 test cases were prepared to evaluate the performance of the proposed system. Finally, system performance evaluation, testing, and user acceptance testing were conducted. User acceptance testing is performed based on seven criteria of evaluation. Selected domain experts are trained and used the system to evaluate how much the KBS meets their requirements. The system on average scored 84.85% based on user acceptance evaluation.

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