References
- I. Cherki, A. Chaker, Z. Djidar, and N. Khalfallah, "A Sequential Hybridization of Genetic Algorithm and Particle Swarm Optimization for the Optimal Reactive Power Flow," 2019.
- K. Drachal, "A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities," 2021.
- A. M. Aibinu, B. S. H, and M. N. C. M. Akachukwu, "A Novel Clustering based Genetic Algorithm ( CGA ) for Robot Route and Functions Optimization."
- X. Zhou, F. Miao, and H. Ma, "Genetic Algorithm with an Improved Initial Population Technique for Automatic Clustering of Low-Dimensional Data," pp. 1-23, 2018, doi: 10.3390/info9040101.
- Y. Deng, Y. Liu, and D. Zhou, "An Improved Genetic Algorithm with Initial Population Strategy for Symmetric TSP," vol. 2015, 2015.
- A. B. Hassanat, V. B. S. Prasath, M. A. Abbadi, S. A. Abuqdari, and H. Faris, "An Improved Genetic Algorithm with a New Initialization Mechanism Based on Regression Techniques," doi: 10.3390/info9070167.
- T. G. Dietterich, "Ensemble methods in machine learning," Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 1857 LNCS, pp. 1-15, 2000, doi: 10.1007/3-540-45014-9_1.
- R. Richman and M. V. Wuthrich, "Nagging predictors," Risks, vol. 8, no. 3, pp. 1-26, 2020, doi: 10.3390/risks8030083.
- Y. Freund and R. E. Schapire, "Experiments with a New Boosting Algorithm," Proc. 13th Int. Conf. Mach. Learn., pp. 148-156, 1996, doi: 10.1.1.133.1040.
- Y. L. Pavlov, "Random forests," Random For., pp. 1-122, 2019, doi: 10.1201/9780429469275-8.
- V. M. Cowton, J. B. Singer, R. J. Gifford, and A. H. Patel, "Predicting the effectiveness of hepatitis C virus neutralizing antibodies by bioinformatic analysis of conserved epitope residues using public sequence data," Front. Immunol., vol. 9, no. JUN, pp. 1-14, 2018, doi: 10.3389/fimmu.2018.01470.
- WHO, "WHO Global Hepatitis Report," 2017, [Online]. Available: http://apps.who.int/iris/bitstream/10665/255016/1/9789241565455-eng.pdf?ua=1.
- CDC, "Hepatitis C," Osp. Magg., p. 2, 2015, doi: 10.1016/j.disamonth.2014.04.002.
- FIND, "Strategy for Hepatitis C 2015-2020," 2014.
- C. W. Shepard, L. Finelli, and M. J. Alter, "Global epidemiology of hepatitis C virus infection.," Lancet. Infect. Dis., vol. 5, no. 9, pp. 558-67, 2005, doi: 10.1016/S1473-3099(05)70216-4.
- Global hepatitis report, 2017. 2017.
- A. Roos et al., "Investigations, findings, and follow-up in patients with chest pain and elevated high-sensitivity cardiac troponin T levels but no myocardial infarction," Int. J. Cardiol., vol. 232, no. June, pp. 111-116, 2017, doi: 10.1016/j.ijcard.2017.01.044.
- M. Jefferies, B. Rauff, H. Rashid, T. Lam, and S. Rafiq, "Update on global epidemiology of viral hepatitis and preventive strategies," World J. Clin. Cases, vol. 6, no. 13, pp. 589-599, 2018, doi: 10.12998/wjcc.v6.i13.589.
- C. T. Wai et al., "A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C," Hepatology, vol. 38, no. 2, pp. 518-526, 2003, doi: 10.1053/jhep.2003.50346.
- P. Halfon et al., "Accuracy of hyaluronic acid level for predicting liver fibrosis stages in patients with hepatitis C virus," Comp. Hepatol., vol. 4, pp. 1-7, 2005, doi: 10.1186/1476-5926-4-6.
- R. Tinati, X. Wang, I. Brown, T. Tiropanis, and W. Hall, "A Streaming Real-Time Web Observatory Architecture for Monitoring the Health of Social Machines," Proc. 24th Int. Conf. World Wide Web - WWW '15 Companion, pp. 1149-1154, 2015, doi: 10.1145/2740908.2743977.
- R. Umar, A. David, and A. Adesiyun, "Observatory system for monitoring hepatitis c development in Nigeria," 2019 15th Int. Conf. Electron. Comput. Comput. ICECCO 2019, no. Icecco, pp. 1-6, 2019, doi: 10.1109/ICECCO48375.2019.9043245.
- A. H. Observatory, "Health Observatories," no. April, 2016.
- J. S. Sartakhti, M. H. Zangooei, and K. Mozafari, "Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA)," Comput. Methods Programs Biomed., vol. 108, no. 2, pp. 570-579, 2012, doi: 10.1016/j.cmpb.2011.08.003.
- T. M. Ghazal et al., "Hep-pred: Hepatitis C staging prediction using fine gaussian SVM," Comput. Mater. Contin., vol. 69, no. 1, pp. 191-203, 2021, doi: 10.32604/cmc.2021.015436.
- D. Sarma et al., "Artificial Neural Network Model for Hepatitis C Stage Detection," EDU J. Comput. Electr. Eng., vol. 1, no. 1, pp. 11-16, 2020, doi: 10.46603/ejcee.v1i1.6.
- A. M. Hashem, M. E. M. Rasmy, K. M. Wahba, and O. G. Shaker, "Single stage and multistage classification models for the prediction of liver fibrosis degree in patients with chronic hepatitis C infection," Comput. Methods Programs Biomed., vol. 105, no. 3, pp. 194-209, 2012, doi: 10.1016/j.cmpb.2011.10.005.
- N. H. Barakat, S. H. Barakat, and N. Ahmed, "Prediction and staging of hepatic fibrosis in children with hepatitis c virus: A machine learning approach," Healthc. Inform. Res., vol. 25, no. 3, pp. 173-181, 2019, doi: 10.4258/hir.2019.25.3.173.
- M. A. Khan, J. E. Soh, M. Maenner, W. W. Thompson, and N. P. Nelson, "A machine-learning algorithm to identify hepatitis C in health insurance claims data," Online J. Public Health Inform., vol. 11, no. 1, pp. 98-99, 2019, doi: 10.5210/ojphi.v11i1.9685.
- D. M. Journal, "An application of multilayer neural network on hepatitis disease diagnosis using approximations of sigmoid activation function Hepatitis disease dataset," vol. 42, no. 2, pp. 150-157, 2015, doi: 10.5798/diclemedj.0921.2015.02.0550.
- W. Mostert and K. M. Malan, "Comparative Analysis," pp. 1-16, 2021.
- S. Wu, Y. Hu, W. Wang, X. Feng, and W. Shu, "Application of Global Optimization Methods for Feature Selection and Machine Learning," vol. 2013, 2013.
- P. L. Lanzi, "Fast feature selection with genetic algorithms: A filter approach," Proc. IEEE Conf. Evol. Comput. ICEC, pp. 537-540, 1997, doi: 10.1109/icec.1997.592369.
- X. Dong, Z. Yu, W. Cao, Y. Shi, and Q. Ma, "A survey on ensemble learning," Front. Comput. Sci., vol. 14, no. 2, pp. 241-258, 2020, doi: 10.1007/s11704-019-8208-z.
- "Ensemble Methods, Foundations and Algorithms.pdf." .
- A. K. Seewald, "Towards a theoretical framework for ensemble classification," IJCAI Int. Jt. Conf. Artif. Intell., no. 3, pp. 1443-1444, 2003.
- P. Pintelas and I. E. Livieris, "Special issue on ensemble learning and applications," Algorithms, vol. 13, no. 6, 2020, doi: 10.3390/A13060140.
- R. Umar, M. M. Boukar, S. Adeshina, and S. Dane, "Machine Learning Approaches for Optimal Parameter Selection for Hepatitis Disease Classification."
- F. E. H. Tay and L. Shen, "A modified Chi2 algorithm for discretization," IEEE Trans. Knowl. Data Eng., vol. 14, no. 3, pp. 666-670, 2002, doi: 10.1109/TKDE.2002.1000349.
- H. Liu and R. Setiono, "Feature Selection via Discretization," vol. 9, no. 4, pp. 1995-1998, 1997.
- H. Liu and R. Setiono, "Chi2: feature selection and discretization of numeric attributes," Proc. Int. Conf. Tools with Artif. Intell., pp. 388-391, 1995, doi: 10.1109/tai.1995.479783.