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Liver disorder detection using variable- neighbor weighted fuzzy K nearest neighbor approach

机译：使用可变邻邻加权模糊K最近邻近的肝脏障碍检测

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摘要

The human liver disorder is a genetic problem due to the habituality of alcohol or effect by the virus. It can lead to liver failure or liver cancer, if not been detected in initial stage. The aim of the proposed method is to detect the liver disorder in initial stage using liver function test dataset. The problem with many real-world datasets including liver disease diagnosis data is class imbalanced. The word imbalance refers to the conditions that the number of observations belongs to one class having more or less than the other class(es). Traditional K- Nearest Neighbor (KNN) or Fuzzy KNN classifier does not work well on the imbalanced dataset because they treat the neighbor equally. The weighted variant of Fuzzy KNN assign a large weight for the neighbor belongs to the minority class data and relatively small weight for the neighbor belongs to the majority class to resolve the issues with data imbalance. In this paper, Variable- Neighbor Weighted Fuzzy K Nearest Neighbor Approach (Variable-NWFKNN) is proposed, which is an improved variant of Fuzzy-NWKNN. The proposed Variable-NWFKNN method is implemented on three real-world imbalance liver function test datasets BUPA, ILPD from UCI and MPRLPD. The Variable-NWFKNN is compared with existing NWKNN and Fuzzy-NWKKNN methods and found accuracy 73.91% (BUPA Dataset), 77.59% (ILPD Dataset) and 87.01% (MPRLPD Dataset). Further, TL_RUS method is used for preprocessing and it improved the accuracy as 78.46% (BUPA Dataset), 78.46% (ILPD Dataset) and 95.79% (MPRLPD Dataset).
机译：由于病毒的酒精或效果的习惯性，人肝障碍是一种遗传问题。如果未在初始阶段检测到，它可以导致肝功能衰竭或肝癌。所提出的方法的目的是使用肝功能测试数据集检测初始阶段的肝脏障碍。许多现实数据集的问题包括肝病诊断数据的类是不平衡的。单词不平衡是指观察人数所属的条件属于一个或多或少的一个类。传统的K-最近邻（KNN）或模糊KNN分类器在不平衡数据集上不适用于良好的数据集，因为它们同样对邻居进行治疗。模糊KNN的加权变体为邻居分配大量重量属于少数类数据，并且邻居的相对较小的重量属于多数类，以解决数据不平衡的问题。在本文中，提出了可变邻居加权模糊K最近邻近（变量-NWFKnN），这是模糊-NWKNN的改进变体。所提出的变量-NWFKM方法是在三个现实世界不平衡肝功能测试数据集Bupa，来自UCI和MPRLPD的ILPD中实现的。将变量-NWFKN与现有的NWKNN和Fuzzy-NWKKNN方法进行比较，发现精度为73.91％（BUPA数据集），77.59％（ILPD DataSet）和87.01％（MPRLPD DataSet）。此外，TL_RUS方法用于预处理，并将其提高为78.46％（BUPA数据集），78.46％（ILPD DataSet）和95.79％（MPRLPD DataSet）。

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