Support Vector Machine Parameter Optimization to Improve Liver Disease Estimation with Genetic Algorithm
Open Access
- 1 April 2020
- journal article
- Published by Politeknik Ganesha in sinkron
- Vol. 4 (2), 106-114
- https://doi.org/10.33395/sinkron.v4i2.10524
Abstract
Liver disease is an important public health problem. Over the past few decades, machine learning has developed rapidly and it has been introduced for application in medical-related. In this study we propose Support Vector Machine optimization parameter with genetic algorithm to get a higher performance of Root Mean Square Error value of SVM in order to estimate the liver disorder. The experiment was carried out in three stages, the first step was to try the three SVM kernels with different combination of parameters manually, The second step was to try some combination of range parameters in the genetic algorithm to find the optimal value in the SVM kernel. The third step is comparing the results of the GA-SVM experiment with other regression methods. The results prove that GA has an influence on improving the performance of GA-SVM which has the lowest RMSE value compared to another regression models.Keywords
This publication has 19 references indexed in Scilit:
- Prediction of contraction scour using ANN and GAFlow Measurement and Instrumentation, 2016
- Diagnosing a disorder in a classification benchmarkPattern Recognition Letters, 2016
- Estimation of wind resources in the coast of Ceará, Brazil, using the linear regression theoryRenewable and Sustainable Energy Reviews, 2014
- Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithmApplied Soft Computing, 2014
- A new hybrid Modified Firefly Algorithm and Support Vector Regression model for accurate Short Term Load ForecastingExpert Systems with Applications, 2014
- Support vector machine applications in the field of hydrology: A reviewApplied Soft Computing, 2014
- Real estate price forecasting based on SVM optimized by PSOOptik, 2014
- Suitability of KNN Regression in the Development of Interaction based Software Fault Prediction ModelsIERI Procedia, 2014
- An experimental investigation of two Wavelet-MLP hybrid frameworks for wind speed prediction using GA and PSO optimizationInternational Journal of Electrical Power & Energy Systems, 2013
- A Critical Study of Selected Classification Algorithms for Liver Disease DiagnosisInternational Journal of Database Management Systems, 2011