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

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).