Utilisation of Machine Learning Techniques in Testing and Training of Different Medical Datasets
Keywords:Disease, Machine Learning Techniques, COVID-19, Symptoms, Medical Datasets
On our planet, chemical waste increases day after day, the emergence of new types of it, as well as the high level of toxic pollution, the difficulty of daily life, the increase in the psychological state of humans, and other factors all have led to the emergence of many diseases that affect humans, including deadly once like COVID-19 disease. Symptoms may appear on a person, and sometimes they may not; some people may know their condition, and others may neglect their health status due to lack of knowledge that may lead to death, or the disease may be chronic for life. In this regard, the author executes machine learning techniques (Support Vector Machine, C5.0 Decision Tree, K-Nearest Neighbours, and Random Forest) due to their influence in medical sciences to identify the best technique that gives the highest level of accuracy in detecting diseases. Thus, this technique will help to recognise symptoms and diagnose them correctly. This article covers a dataset from the UCI machine learning repository, namely the Wisconsin Breast Cancer dataset, Chronic Kidney disease dataset, Immunotherapy dataset, Cryotherapy dataset, Hepatitis dataset and COVID-19 dataset. In the results section, a comparison is made between the execution of each technique to find out which one is the best and which one is the worst in the performance of analysis related to the dataset of each disease.
Grassly N. C. and Fraser C., “Seasonal infectious disease epidemiology,” Proceedings. Biological sciences, vol.273, no.1600, pp: 2541–2550, July 2006. https://doi.org/10.1098/rspb.2006.3604
Tate M. D., Deng Y., Jones J. E., Anderson G. P., Brooks A. G., and Reading P. C., “Neutrophils Ameliorate Lung Injury and the Development of Severe Disease during Influenza Infection,” The Journal of Immunology, vol. 183, pp:7441-7450, November 2009. https://doi.org/10.4049/jimmunol.0902497
Pandey S. C., “Data Mining Techniques for Medical Data: A Review,” In Proceedings of International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), pp:1-12, Paralakhemundi, India, 3-5 October 2016. https://doi.org/10.1109/SCOPES.2016.7955586
Jia Q., Guo Y., Wang G., and Barnes S. J., “Big Data Analytics in the Fight against Major Public Health Incidents (Including COVID-19): A Conceptual Framework,” International Journal of Environmental Research and Public Health, vol.17, no.6161, pp:1-20, August 2020. https://doi.org/10.3390/ijerph17176161
Jones L. D., Golan D., Hanna S. A., and Ramachandran M., “Artificial intelligence, machine learning and the evolution of healthcare: A bright future or cause for concern?,” Bone & Joint Research, vol.7, no.33,pp:223-225, March 2018. https://doi.org/10.1302/2046-3758.73.BJR-2017-0147.R1
Schmidt J., Marques M. R. G., Botti S., and Marques M. A. L., “Recent Advances and Applications of Machine Learning in Solid-State Materials Science,” NPJ Computational Materials, vol.5, no.83, pp:1-11, August 2019. https://doi.org/10.1038/s41524-019-0221-0
Battineni G., Sagaro G. G., Chinatalapudi N., Amenta F., “Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis,” Journal of Personalized Medicine, vol.10, no.21, pp:1-11, March 2020. https://doi.org/10.3390/jpm10020021
Pham T. D., “Classification of COVID-19 chest X-rays with deep learning: new models or fine tuning?” Health Information Science and Systems, vol.9, no. 2, November 2020. https://doi.org/10.1007/s13755-020-00135-3
Mijwil, M. M., “Implementation of Machine Learning Techniques for the Classification of Lung X-Ray Images Used to Detect COVID-19 in Humans,” Iraqi Journal of Science, vol.62, no.6., pp: 2099-2109, 2 July 2021. https://doi.org/10.24996/ijs.2021.62.6.35.
Mijwil, M. M. and Al-Zubaidi, E. A., “Medical Image Classification for Coronavirus Disease (COVID-19) Using Convolutional Neural Networks,” Iraqi Journal of Science, vol.62, no.8, pp: 2740-2747, 31 August 2021. https://doi.org/10.24996/ijs.2021.62.8.27.
Mijwil, M. M., Alsaadi, A. S, and Aggarwal K., “Differences and Similarities Between Coronaviruses: A Comparative Review,” Asian Journal of Pharmacy, Nursing and Medical Sciences, vol.9, no.4, pp:49-61. 10 September 2021. https://doi.org/10.24203/ajpnms.v9i4.6696
Borkowski A. A., Viswanadhan N. A., Thomas L. B., Guzman R. D., Deland L. A., and Mastorides S. M., “Using Artificial Intelligence for COVID-19 Chest X-ray Diagnosis,” Federal practitioner: for the health care professionals of the VA, DoD, and PHS, vol.37, no.9, pp: 398–404, September 2020. https://doi.org/10.12788/fp.0045
Sidey-Gibbons J. A. M. and Sidey-Gibbons C. J., “Machine Learning in Medicine: A Practical Introduction,” BMC Medical Research Methodology, vol.19, no.64, pp:1-18, March 2019. https://doi.org/10.1186/s12874-019-0681-4
Mishra A. K., Das S. K., Roy P., and Bandyopadhyay S., “Identifying COVID19 from Chest CT Images: A Deep Convolutional Neural Networks Based Approach,” Journal of Healthcare Engineering, vol.2020, ID. 8843664, pp:1-7, August 2020. https://doi.org/10.1155/2020/8843664
Aswal S., Ahuja N. J., and Ritika, “Experimental analysis of traditional classification algorithms on bio medical dtatasets,” In Proceedings of International Conference on Next Generation Computing Technologies (NGCT), pp:1-6, Dehradun, India, 14-16 October 2016. https://doi.org/10.1109/NGCT.2016.7877478
Islam M., Iqbal I., Haque R., and Hasan K., “Prediction of breast cancer using support vector machine and K-Nearest neighbors,” In Proceedings of International Conference on Region 10 Humanitarian Technology (R10-HTC), pp:1-6, Dhaka, Bangladesh,21-23 December 2017. https://doi.org/10.1109/R10-HTC.2017.8288944
Cahyani N., and Muslim M. A., “Increasing Accuracy of C4.5 Algorithm by Applying Discretization and Correlation-based Feature Selection for Chronic Kidney Disease Diagnosis,” Journal of Telecommunication, Electronic and Computer Engineering, Vol.12 No.1, pp:25-32, March 2020.
Eedi H. and Kolla M. “Machine Learning Approaches for Healthcare Data Analysis,” Journal of Critical Reviews, vol.7, no.4, pp:806-81,1 February 2020. http://dx.doi.org/10.31838/jcr.07.04.149
Kumar A., Sinha N., and Bhardwaj A., “A Novel Fitness Function in Genetic Programming for Medical Data Classification,” Journal of Biomedical Informatics, vol. 112, December 2020. https://doi.org/10.1016/j.jbi.2020.103623
Dua D. and Graff C., UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science, 2019.
Street W. N., Wolberg W. H., and Mangasarian O. L., “Nuclear feature extraction for breast tumor diagnosis,” IS&T/SPIE 1993 International Symposium on Electronic Imaging: Science and Technology, vol.1905, pp:861-870, California, United States, 1993.
Thomas R., Kanso A., and Sedor J. R., “Chronic Kidney Disease and Its Complications,” Primary Care: Clinics in Office Practice, vol.35, pp: 329–344, 2008. https://doi.org/10.1016/j.pop.2008.01.008
Khozeimeh F., Azad F. J., Oskouei Y. M., M. Jafari, S. Tehranian S., Alizadehsani R., and Layegh P., “Intralesional Immunotherapy Compared to Cryotherapy in The Treatment of Warts,” International Journal of Dermatology, vol.56, pp:474–478. 2017, https://doi.org/10.1111/ijd.13535
Khozeimeh F., Alizadehsani R., Roshanzamir M., Khosravi A., Layegh P., and Nahavandi S., “An expert system for selecting wart treatment method,” Computers in Biology and Medicine, vol. 81, pp:167-175, February 2017. https://doi.org/10.1016/j.compbiomed.2017.01.001
Cestnik G., Konenenko I., and Bratko I., Assistant-86: A Knowledge-Elicitation Tool for Sophisticated Users. In: Bratko, I. and Lavrac, N., Eds., Progress in Machine Learning, Sigma Press, Wilmslow, pp: 31-45, 1987.
Wua Y., Chena C., and Chan Y., “The outbreak of COVID-19: An overview,” Journal of the Chinese Medical Association, vol.83, no.3, pp:217-220. March 2020. https://doi.org/10.1097/JCMA.0000000000000270
Yu W., Liu T., Valdez R., Gwinn M., and Khoury M. J., “Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes,” BMC Medical Informatics and Decision Making, vol.10, no.16, pp:1-7, March 2010. https://doi.org/10.1186/1472-6947-10-16
Rajeswari S., and Suthendran K., “C5.0: Advanced Decision Tree (ADT) classification model for agricultural data analysis on cloud,” Computers and Electronics in Agriculture, vol.156, pp:530-539, December 2018. https://doi.org/10.1016/j.compag.2018.12.013
Zhang Z., “Introduction to machine learning: k-nearest neighbors,” Annals of Translational Medicine, vol.4, no.11, pp:1-7, June 2016. https://doi.org/10.21037/atm.2016.03.37
Biau G., and Editor: Yu B., “Analysis of a Random Forests Model,” Journal of Machine Learning Research, vol.13, pp:1063-1095, April 2012.
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