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首页> 外文期刊>Ethiopian journal of health sciences >Developing a Prototype Knowledge-Based System for Diagnosis and Treatment of Diabetes Using Data Mining Techniques
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Developing a Prototype Knowledge-Based System for Diagnosis and Treatment of Diabetes Using Data Mining Techniques

机译：使用数据挖掘技术开发基于原型知识的系统，用于使用数据挖掘技术进行诊断和治疗糖尿病

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Background Diabetes is a disease that affects the body's ability to produce or use insulin. A total of 425 million people are suffering from diabetes in the world. Of this, more than 16 million people live in the Africa Region, which is estimated to be around 41 million by 2045. The main objective of this study was to design and develop a prototype knowledge-based system using data mining techniques for diagnosis and treatment of diabetes. Methods For this study, experimental research design was employed, and the researchers used domain expert knowledge as a supplement of data mining techniques whereby three classification algorithms in WEKA; namely J48, PART and JRip were used, and finally the researchers decided to use the results of J48 classification algorithm. Ultimate Visual basic studio 2013 (Vb.net) was used to store knowledge and as front side of prototype. Common lisp prolog (Clisp) was used for obtained knowledge back end coding. Results Using a decision tree algorithm; namely J48, 2512 (95.1515%) of the instances were classified correctly, and 128 (4.8485 %) were classified incorrectly. The second most performing model was generated by JRip Classier. This model scored the 94.7348% accuracy on the general data to classify the status of diabetic patient datasets. It classified the 2501 instances of the records correctly. Conclusion The J48 model was the best performing model with the best accuracy of results.
机译：背景技术糖尿病是一种影响身体生产或使用胰岛素的能力的疾病。共有42500万人患有世界糖尿病。其中，超过1600万人生活在非洲地区，估计到2045年的4100万左右。本研究的主要目标是使用数据挖掘技术进行设计和开发基于原型知识的系统，用于诊断和治疗糖尿病。该研究的方法，采用了实验研究设计，研究人员使用域专家知识作为数据挖掘技术的补充，其中魏卡中的三种分类算法;即使用J48，部分和Jrip，最后研究人员决定使用J48分类算法的结果。 Ultimate Visual Basic Studio 2013（VB.Net）用于存储原型的知识和作为前侧。常见的LISP PROLOG（CLISP）用于获得知识后端编码。结果使用决策树算法;即J48,2512（95.1515％）的实例被正确分类，128（4.8485％）被错误分类。第二个最具表演模型由Jrip Claserier生成。该模型在一般数据上得分为94.7348％的准确性，以分类糖尿病患者数据集的状态。它正确分类了2501个记录实例。结论J48模型是表现最佳的结果，结果最佳。

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