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.
Reference22 articles.
1. Beeching, N., & Dassanayake, A. (2019). Tropical liver disease. Medicine (United Kingdom), 47(11), 758-763. https://doi.org/10.1016/j.mpmed.2019.08.001
2. Detection of Cancer in Lung With K-NN Classification Using Genetic Algorithm;Bhuvaneswari;Procedia Materials Science 10(Cnt 2014),2015
3. Chen, R., Liang, C.-Y., Hong, W.-C., & Gu, D.-X. (2015). Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm. Applied Soft Computing, 26, 435-443. https://doi.org/10.1016/j.asoc.2014.10.022
4. Goyal, R., Chandra, P., & Singh, Y. (2014). Suitability of KNN Regression in the Development of Interaction Based Software Fault Prediction Models. IERI Procedia, 6, 15-21. Retrieved from http://dx.doi.org/10.1016/j.ieri.2014.03.004
5. Harafani, H., & Maulana, A. (2019). Penerapan Algoritma Genetika pada Support Vector Machine Sebagai Pengoptimasi Parameter untuk Memprediksi Kesuburan. V(1), 51-59.
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