Determine the Prevalence of Hepatitis B and C During Pregnancy by Using Machine Learning Algorithm

Authors

  • Awad Bin Naeem Department of Computer Science, NCB&E, Multan, Pakistan
  • Biswaranjan Senapati Department of Computer Science and Data Science, Parker Hannifin Corp, Chicago, IL 60503, USA
  • Ghayth AL Mahadin Faculty of Information Technology / Al Ahliyya Amman University Country: Jordan
  • Vivek Ghulaxe Department of Computer Science in Publix USA
  • Friban Almeida Department of Engineering Manager software development, USA
  • Sakiul Islam Sudman Department of Electrical Engineering, University of Texas, Arlington, TX 76019, USA
  • Muhammad Imran Ghafoor Department of Engineering, Pakistan Television Corporation Lahore Pakistan

Keywords:

Medical Data Set, Hypothyroidism Symptoms, SVM, KNN, Naive Bayes

Abstract

South Punjab has a high frequency of Hepatitis B and C among pregnant women, making it a highly endemic region. Lack of knowledge, drinking tap water, dental procedures, and past surgery all raise the risk of infection. The anticipated model's accuracy is 86.32%. A low socioeconomic position and a history of surgical therapy are also important considerations. Using medical data sets, this research attempted to illustrate the performance of ensemble approaches in identifying Hepatitis infection. Three algorithms were used, and the suggested model was evaluated using data from actual hepatitis patients. The research discovered that 64% of pregnant women in rural regions had Hepatitis B and C, whereas 36% of pregnant women in urban areas had Hepatitis B and C. Hepatitis C was more prevalent in cities than in rural regions. The research underlines the necessity of correct diagnosis in clinical decision support systems, as well as data set compatibility.

Downloads

Download data is not yet available.

References

Arshad, I., Dutta, C., Choudhury, T., & Thakral, A. (2018). Liver Disease Detection Due to Excessive Alcoholism Using Data Mining Techniques. Proceedings on 2018 International Conference on Advances in Computing and Communication Engineering, ICACCE 2018, June, 163–168. https://doi.org/10.1109/ICACCE.2018.8441721

Bayrak, E. A., Kirci, P., & Ensari, T. (2019). Performance Analysis of Machine Learning Algorithms and Feature Selection Methods on Hepatitis Disease. International Journal of Multidisciplinary Studies and Innovative Technologies, 3(2), 135–138.

Soomro, A.M.; Naeem, A.B.; Senapati, B.; Bashir, K.; Pradhan, S.; Maaliw, R.R.; Sakr, H.A. Constructor Development: Predicting Object Communication Errors. In Proceedings of the 2023 IEEE International Conference on Emerging Trends in Engineering, Sciences and Technology (ICES&T), Bahawalpur, Pakistan, 9–11 January 2023; pp. 1–7

Naeem, Awad Bin, Biswaranjan Senapati, Md. Sakiul Islam Sudman, Kashif Bashir, and Ayman E. M. Ahmed. 2023. "Intelligent Road Management System for Autonomous, Non-Autonomous, and VIP Vehicles" World Electric Vehicle Journal 14, no. 9: 238. https://doi.org/10.3390/wevj14090238.

Hofstraat, S. H. I., Falla, A. M., Duffell, E. F., Hahné, S. J. M., Amato-Gauci, A. J., Veldhuijzen, I. K., & Tavoschi, L. (2017). Current prevalence of chronic hepatitis B and C virus infection in the general population, blood donors and pregnant women in the EU/EEA: A systematic review. Epidemiology and Infection, 145(14), 2873–2885. https://doi.org/10.1017/S0950268817001947

Hussien, I., Omer, S., Oweis, N. E., & Snášel, V. (2016). Feature selection using semi discrete decomposition and singular value decompositions. Advances in Intelligent Systems and Computing, 450, 87–97. https://doi.org/10.1007/978-3-319-33609-1_8

Hussien, S. O., Elkhatem, S. S., Osman, N., & Ibrahim, A. O. (2018). A review of data mining techniques for diagnosing hepatitis. Proceedings of: 2017 Sudan Conference on Computer Science and Information Technology, SCCSIT 2017, 2017-Novem, 1–6. https://doi.org/10.1109/SCCSIT.2017.8293064

Naeem, A. B., Senapati, B., Chauhan, A. S., Kumar, S., Orosco Gavilan, J. C., & F. Abdel-Rehim, W. M. (2023, February 17). Deep Learning Models for Cotton Leaf Disease Detection with VGG-16 | International Journal of Intelligent Systems and Applications in Engineering. Deep Learning Models for Cotton Leaf Disease Detection With VGG-16 | International Journal of Intelligent Systems and Applications in Engineering. https://www.ijisae.org/index.php/IJISAE/article/view/2710

Kaur, S., & Bawa, R. K. (2015). Future Trends of Data Mining in Predicting the Various Diseases in Medical Healthcare System. International Journal of Energy, Information and Communications, 6(4), 17–34. https://doi.org/10.14257/ijeic.2015.6.4.02

Komal Kumar, N., & Vigneswari, D. (2019). Hepatitis- infectious disease prediction using classification algorithms. Research Journal of Pharmacy and Technology, 12(8), 3720–3725. https://doi.org/10.5958/0974-360X.2019.00636.X

Naeem, A. B. ., Senapati, B. ., Chauhan, A. S. ., Makhija, M. ., Singh, A. ., Gupta, M. ., Tiwari, P. K. ., & Abdel-Rehim, W. M. F. . (2023). Hypothyroidism Disease Diagnosis by Using Machine Learning Algorithms. International Journal of Intelligent Systems and Applications in Engineering, 11(3), 368–373. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/3178

Naeem, A.B., Soomro, A.M., Saim, H.M. et al. Smart road management system for prioritized autonomous vehicles under vehicle-to-everything (V2X) communication. Multimed Tools Appl (2023). https://doi.org/10.1007/s11042-023-16950-1

Noda, Y., Kawaguchi, T., Kuromatsu, R., Komukai, S., Nakano, M., Niizeki, T., Koga, H., Kawaguchi, A., & Torimura, T. (2019). Prognostic profile of patients with non-viral hepatocellular carcinoma: A comparative study with hepatitis C virus-related hepatocellular carcinoma using data mining analysis. Oncology Letters, 18(1), 227–236. https://doi.org/10.3892/ol.2019.10285

Naeem, A. B., Khalid, F., Soomro, A. M., Del Mundo, A. D., Zaidi, A., Senapati, B., & Doshi, O. P. (2023, March 29). Early Gender Identification of Date Palm Using Machine Learning | Journal of Computing & Biomedical Informatics. Early Gender Identification of Date Palm Using Machine Learning | Journal of Computing & Biomedical Informatics. https://www.jcbi.org/index.php/Main/article/view/147

Trishna, T. I., Emon, S. U., Ema, R. R., Sajal, G. I. H., Kundu, S., & Islam, T. (2019). Detection of Hepatitis (A, B, C and E) Viruses Based on Random Forest, K-nearest and Naïve Bayes Classifier. 2019 10th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2019, 1–7. https://doi.org/10.1109/ICCCNT45670.2019.8944455

Soomro, A. M., Naeem, A. B., Shahzad, K., Madni, A. M., Del Mundo, A. D., Sajid, M., & Baloch, M. A. (2022, December 29). Forecasting Cotton Whitefly Population Using Deep Learning | Journal of Computing & Biomedical Informatics. Forecasting Cotton Whitefly Population Using Deep Learning | Journal of Computing & Biomedical Informatics. https://doi.org/10.56979/401/2022/67

Naeem, A.B. et al. (2023) ‘Intelligent Four-way Crossroad Safety Management for autonomous, non-autonomous and VIP vehicles’, 2023 IEEE International Conference on Emerging Trends in Engineering, Sciences and Technology (ICES&T). doi:10.1109/icest56843.2023.10138829.

Senapati, B., Rawal, B.S. (2023). Adopting a Deep Learning Split-Protocol Based Predictive Maintenance Management System for Industrial Manufacturing Operations. In: Hsu, CH., Xu, M., Cao, H., Baghban, H., Shawkat Ali, A.B.M. (eds) Big Data Intelligence and Computing. DataCom 2022. Lecture Notes in Computer Science, vol 13864. Springer, Singapore. https://doi.org/10.1007/978-981-99-2233-8_2

Biswaranjan Senapati, Bharat S. Rawal, Quantum Communication with RLP Quantum Resistant Cryptography in Industrial Manufacturing, Cyber Security and Applications, 2023, 100019, ISSN 2772-9184, https://doi.org/10.1016/j.csa.2023.100019

Sabugaa, M., Senapati, B., Kupriyanov, Y., Danilova, Y., Irgasheva, S., Potekhina, E. (2023). Evaluation of the Prognostic Significance and Accuracy of Screening Tests for Alcohol Dependence Based on the Results of Building a Multilayer Perceptron. In: Silhavy, R., Silhavy, P. (eds) Artificial Intelligence Application in Networks and Systems. CSOC 2023. Lecture Notes in Networks and Systems, vol 724. Springer, Cham. https://doi.org/10.1007/978-3-031-35314-7_23

Moutsinas, G.A., Esponda-Pérez, J.A., Senapati, B., Sanyal, S., Patra, I., Karnaukhov, A. (2023). Application of Virtual Reality in Education. In: Silhavy, R., Silhavy, P. (eds) Software Engineering Research in System Science. CSOC 2023. Lecture Notes in Networks and Systems, vol 722. Springer, Cham. https://doi.org/10.1007/978-3-031-35311-6_33

B. Senapati, J. R. Talburt, A. Bin Naeem and V. J. R. Batthula, "Transfer Learning Based Models for Food Detection Using ResNet-50," 2023 IEEE International Conference on Electro Information Technology (eIT), Romeoville, IL, USA, 2023, pp. 224-229, doi: 10.1109/eIT57321.2023.10187288

B. Senapati, R. Regin, S. S. Rajest, P. Paramasivan, and A. J. Obaid, “Quantum Dot Solar Cells and Their Role inRevolutionizing Electrical Energy Conversion Efficiency,” FMDB Transactions on Sustainable Energy Sequence, vol. 1, no.1, pp. 49–59, 2023

(PDF) Quantum Dot Solar Cells and Their Role in Revolutionizing Electrical Energy Conversion Efficiency. Available from: https://www.researchgate.net/publication/375039500_Quantum_Dot_Solar_Cells_and_Their_Role_in_Revolutionizing_Electrical_Energy_Conversion_Efficiency [accessed Dec 05 2023].

Downloads

Published

29.01.2024

How to Cite

Naeem, A. B. ., Senapati, B. ., Mahadin, G. A. ., Ghulaxe, V. ., Almeida, F. ., Sudman, S. I. ., & Ghafoor, M. I. . (2024). Determine the Prevalence of Hepatitis B and C During Pregnancy by Using Machine Learning Algorithm . International Journal of Intelligent Systems and Applications in Engineering, 12(13s), 744–751. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4704

Issue

Vol. 12 No. 13s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.