Proceeding International IOP Publishing Gasal 2020

research
  • 09 Apr
  • 2023

Proceeding International IOP Publishing Gasal 2020

Liver disease is an important public health problem. Over the past view decades
machine learning has develop rapidly, and it has been introduced for application in medicalrelated fields. In this study we use neural network method to solve regression task of liver
disorder dataset. Genetic algorithm applied for optimize NN parameters to improve the
estimation performance value. NN-GA performance results show the most superior value
compared to another methods.

Unduhan

 

REFERENSI

[1] Z. Yao, J. Li, Z. Guan, Y. Ye, and Y. Chen, “Liver disease screening based on densely connected deep neural
networks,” Neural Networks, vol. 123, pp. 299–304, 2020.
[2] B. Venkata Ramana, M. S. P. Babu, and N. . Venkateswarlu, “A Critical Study of Selected Classification
Algorithms for Liver Disease Diagnosis,” Int. J. Database Manag. Syst., vol. 3, no. 2, pp. 101–114, 2011.
[3] D. A. Kelly, “Liver and biliary disease in childhood,” Med. (United Kingdom), vol. 47, no. 12, pp. 782–786,
2019.
[4] N. Beeching and A. Dassanayake, “Tropical liver disease,” Med. (United Kingdom), vol. 47, no. 11, pp.
758–763, 2019.
[5] A. Ghoneim, G. Muhammad, and M. S. Hossain, “Cervical cancer classification using convolutional neural
networks and extreme learning machines,” Futur. Gener. Comput. Syst., vol. 102, pp. 643–649, 2020.
[6] K. Malathi and K. Sivaranjani, “A Hybrid Approach for the Fertility Rate Analysis In Human Beings Using
Classification Algorithms,” vol. 3, no. 6, pp. 609–616, 2018.
[7] R. Jafari-Marandi, S. Davarzani, M. Soltanpour Gharibdousti, and B. K. Smith, “An optimum ANN-based
breast cancer diagnosis: Bridging gaps between ANN learning and decision-making goals,” Appl. Soft
Comput. J., vol. 72, pp. 108–120, 2018.
[8] A. J. Sahoo and Y. Kumar, “Seminal quality prediction using data mining methods,” vol. 22, pp. 531–545,
2014.
[9] R. Goyal, P. Chandra, and Y. Singh, “Suitability of KNN Regression in the Development of Interaction
Based Software Fault Prediction Models,” IERI Procedia, vol. 6, pp. 15–21, 2014.
[10] K. P. Singh, S. Gupta, A. Kumar, and S. P. Shukla, “Linear and nonlinear modeling approaches for urban
air quality prediction,” Sci. Total Environ., vol. 426, pp. 244–255, 2012.
[11] S. Zhang, Z. Deng, D. Cheng, and X. Zhu, “Efficient k NN Classification Algorithm for Big Data,”
Neurocomputing, 2015
[12] S. Lee, P. Kang, and S. Cho, “Neurocomputing Probabilistic local reconstruction for k -NN regression and its
application to virtual metrology in semiconductor manufacturing,” Neurocomputing, vol. 131, pp. 427–439,
2014.
[13] D. Balram, K. Y. Lian, and N. Sebastian, “Air quality warning system based on a localized PM2.5 soft sensor
using a novel approach of Bayesian regularized neural network via forward feature selection,” Ecotoxicol.
Environ. Saf., vol. 182, no. April, p. 109386, 2019.
[14] R. Song, X. Zhang, C. Zhou, J. Liu, and J. He, “Predicting TEC in China based on the neural networks
optimized by genetic algorithm,” Adv. Sp. Res., vol. 62, no. 4, pp. 745–759, 2018.
[15] N. A. HAMID, N. M. NAWI, R. GHAZALI, and M. N. M. SALLEH, “Solving Local Minima Problem in
Back Propagation Algorithm Using Adaptive Gain, Adaptive Momentum and Adaptive Learning Rate on
Classification Problems,” Int. J. Mod. Phys. Conf. Ser., vol. 09, pp. 448–455, 2012.
[16] Z. Tao, L. Huiling, W. Wenwen, and Y. Xia, “GA-SVM based feature selection and parameter optimization
in hospitalization expense modeling,” Appl. Soft Comput. J., vol. 75, pp. 323–332, 2019.
[17] J. McDermott and R. S. Forsyth, “Diagnosing a disorder in a classification benchmark,” Pattern Recognit.
Lett., vol. 73, pp. 41–43, 2016.
[18] H. Harafani, “Support Vector Machine Parameter Optimization to Improve Liver Disease Estimation with
Genetic Algorithm,” SinkrOn, vol. 4, no. 2, p. 106, 2020

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