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An euclidean distance based KNN computational method for assessing degree of liver damage

机译：基于欧氏距离的KNN计算方法评估肝损伤程度

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摘要

Liver is one of the vital organs of human body. It performs number of metabolic functions that are essential for living a healthy life. Early diagnosis of liver disease is a difficult task because the symptoms are more visible in later stages of the damage. Appropriate evaluation of patients becomes a challenge for clinicians which eventually make the disease more alarming. This study according aims to construct an intelligent computing method for classifying various degree of liver damage. Correct identification of degree of liver damage will help the physicians to give appropriate amount of dose to liver patients. For implementation, linear discriminant analysis (LDA), diagonal linear discriminant analysis (DLDA), quadratic discriminant analysis (QDA), diagonal quadratic discriminant analysis (DQDA), classification and regression tree (CART) and k-nearest neighbors (KNN) are deployed. Attained simulation results demonstrated that euclidean distance metric based KNN computational method outperforms all and has given remarkably good results with a prediction accuracy of 92.53%.
机译：肝脏是人体的重要器官之一。它执行许多对于健康生活至关重要的代谢功能。肝病的早期诊断是一项艰巨的任务，因为在损伤的后期阶段症状更加明显。对患者进行适当的评估成为临床医生的挑战，这最终使疾病更加令人震惊。本研究旨在构建一种智能计算方法，以对各种程度的肝损伤进行分类。正确识别肝损害程度将有助于医生为肝病患者提供适量的剂量。为了实施，部署了线性判别分析（LDA），对角线性判别分析（DLDA），二次判别分析（QDA），对角二次判别分析（DQDA），分类和回归树（CART）和k最近邻（KNN） 。仿真结果表明，基于欧氏距离度量的KNN计算方法优于所有方法，并给出了非常好的结果，预测精度为92.53％。

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