Impact of Genetic Optimization on the Prediction Performance of Case-Based Reasoning Algorithm in Liver Disease

doi:10.23940/ijpe.17.04.p2.348361

Abstract

Abstract:

Liver illness is the most hazardous ailment that influences a large number of individuals consistently and ends man's life. An effective diagnosis model is required in the process of liver disease treatment. This study accordingly aims to employ Case-Based Reasoning (CBR) methodology supported by Genetic Algorithm (GA) to optimize the prediction results of liver disease and to analyze their performances on different datasets. CBR methodology has been implemented to find the prediction results of liver disease for different datasets. We proposed a GA-based CBR framework to compare its performance with CBR in order to observe how effective it is at predicting liver illness. CBR prediction accuracy is very low so it is not very much appreciated. The proposed GA-CBR integrated model outperforms the CBR model by achieving better accuracy for all used datasets of liver disease. In this optimization of weights of features and selection of suitable instances are done simultaneously rather than separately. This leads to better prediction performance as compare to independent models. The outcome of this model illustrates that performance of usual CBR enhances fundamentally by utilizing our integrated GA-CBR model approach.

Submitted on March 17, 2017; First Revised on April 22, 2017; Second Revised on May 22, 2017; Accepted on May 24, 2017
References: 41

Sakshi Takkar Aman Singh. Impact of Genetic Optimization on the Prediction Performance of Case-Based Reasoning Algorithm in Liver Disease [J]. Int J Performability Eng, 2017, 13(4): 348-361.

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