Diagnosis, visualisation and analysis of COVID-19 using Machine learning
DOI:
https://doi.org/10.22399/ijcesen.826Keywords:
COVID-19, Machine Learning, Streamlit, KaggleAbstract
The Focal point of this paper is to point out or analyse the different kinds of symptoms and other complications COVID-19 Positive and Negative patients undergo. Coronaviruses are a club of viruses that attack humans with respiratory illness and their impact ranges from mild cold, fever, and dry cough to severe breathing problems, fatigue, chest pain and some other chronic problems. The objective of this research is to analyse the various chronic and other complications undergone by a COVID-19 patient. By considering most standard symptoms (given by WHO and Ministry of Health, govt of India), the data is collected from a renowned data repository called Kaggle and employed the best data analytical techniques to clean it so that it must befits our higher Machine Learning prediction aspirations. In this study, Ensemble machine learning models have been used, which take user input on some of the pre-defined approved standard symptoms and predict whether COVID-19 is present or not. The developed Machine Learning model cannot be left out like this, without any proper interface for duly picking up each data from the users, so we managed to reach out to a best and weighted framework termed Streamlit, for transforming our Machine Learning model into a fully-fledged and dual- faceted (Fill out the data manually by going into each cell or directly drop patient data in CSV file format) Web Application.
References
Phu, D. N., Vinh, P. C., & Quoc, N. K. (2023). Enhanced Diagnosis of Influenza and COVID-19 Using Machine Learning. EAI Endorsed Transactions on Context-aware Systems and Applications, 9. DOI: https://doi.org/10.4108/eetcasa.v9i1.4030
Dawaba, A. M., Amer, H. M., Saleh, A. I., & Abo-Elsoud, M. A. (2023). Covid-19 Detection Using Machine Learning. Mansoura Engineering Journal, 48(3), 10. DOI: https://doi.org/10.58491/2735-4202.3064
Wei, J. W. T., Pheng, H. S., & Haur, O. K. (2023). Computed Tomography Image Analysis on COVID-19 Cases using Machine Learning Approaches. Journal of Advanced Research in Applied Sciences and Engineering Technology, 32(2), 404-416. DOI: https://doi.org/10.37934/araset.32.2.404416
Hussein, A. N., Makki, S. V. A. D., & Al-Sabbagh, A. (2023). Comprehensive study: machine learning approaches for COVID-19 diagnosis. International Journal of Electrical and Computer Engineering (IJECE), 13(5), 5681-5695. DOI: 10.11591/ijece.v13i5.pp5681-5695
Knapińska, Z., Mulawka, J., & Kierzkiewicz, M. (2023). Diagnosis and Proposed Treatment for COVID-19 Patients Based on Deep Learning Analysis of Computed Tomography Images. Applied Sciences, 13(13), 7565. DOI: https://doi.org/10.3390/app13137565
Das, S., Ayus, I., & Gupta, D. (2023). A comprehensive review of COVID-19 detection with machine learning and deep learning techniques. Health and Technology, 13(4), 679-692. DOI: https://doi.org/10.1007/s12553-023-00757-z
Dabbagh, R., Jamal, A., Masud, J. H. B., Titi, M. A., Amer, Y. S., Khayat, A., ... & Temsah, M. H. (2023). Harnessing machine learning in early COVID-19 detection and prognosis: a comprehensive systematic review. Cureus, 15(5). DOI: 10.7759/cureus.38373
Sharma, S., Gupta, Y. K., & Mishra, A. K. (2023). Analysis and prediction of COVID-19 multivariate data using deep ensemble learning methods. International Journal of Environmental Research and Public Health, 20(11), 5943. DOI: https://doi.org/10.3390/ijerph20115943
Alqaissi, E., Alotaibi, F., & Ramzan, M. S. (2023). Graph data science and machine learning for the detection of COVID-19 infection from symptoms. PeerJ Computer Science, 9, e1333. DOI: 10.7717/peerj-cs.1333
Solayman, S., Aumi, S. A., Mery, C. S., Mubassir, M., & Khan, R. (2023). Automatic COVID-19 prediction using explainable machine learning techniques. International Journal of Cognitive Computing in Engineering, 4, 36-46. DOI: https://doi.org/10.1016/j.ijcce.2023.01.003
Ali, S., Hussain, A., Bhattacharjee, S., Athar, A., & Kim, H. C. (2022). Detection of Covid-19 in x-ray images using densely connected squeeze convolutional neural network (dcscnn): focusing on interpretability and explainability of the black box model. Sensors, 22(24), 9983. DOI: https://doi.org/10.3390/s22249983
Yuen, M.-C., Cheng, W.-F., & Wong, H.-H. (2022). Data visualization and analysis with machine learning for the USA’s COVID-19 prediction. In Fuzzy Systems and Data Mining VIII (pp. 181–190). IOS Press. DOI:
3233/FAIA220383
Wu, S., et al. (2023). Deep learning and medical imaging for COVID-19 diagnosis: A comprehensive survey. arXiv preprint arXiv:2302.06611.
Alqahtani, A., Zahoor, M. M., Nasrullah, R., Fareed, A., Cheema, A. A., Shahrose, A., ... & Rahman, S. (2022). Computer aided covid-19 diagnosis in pandemic era using CNN in chest x-ray images. Life, 12(11), 1709. DOI: https://doi.org/10.3390/life12111709
Hussain, M., Islam, A., & Turi, J. A. (2022). Machine learning-driven approach for a COVID-19 warning system. Electronics, November.
Andrade, E. C. D., Pinheiro, P. R., Barros, A. L. B. D. P., Nunes, L. C., Pinheiro, L. I. C., Pinheiro, P. G. C. D., & Holanda Filho, R. (2022). Towards machine learning algorithms in predicting the clinical evolution of patients diagnosed with covid-19. Applied Sciences, 12(18), 8939. DOI: https://doi.org/10.3390/app12188939
Alnedawe, S. M., & Aljobouri, H. K. (2023). A New Model Design for Combating COVID-19 Pandemic Based on SVM and CNN Approaches. Baghdad Science Journal, 20(4), 1402-1402. DOI: 10.21123/bsj/.2023.7403
Almotairi, K. H., Hussein, A. M., Abualigah, L., Abujayyab, S. K., Mahmoud, E. H., Ghanem, B. O., & Gandomi, A. H. (2023). Impact of artificial intelligence on COVID-19 pandemic: a survey of image processing, tracking of disease, prediction of outcomes, and computational medicine. Big Data and Cognitive Computing, 7(1), 11.
Aazad, S. K., Saini, T., Ajad, A., Chaudhary, K., & Elsayed, E. E. (2024). Deciphering Blood Cells-Method for Blood Cell Analysis using Microscopic Images. Journal of Modern Technology, 9-18.
Elsayed, E. E., Hayal, M. R., & Juraev, D. A. (2024). Ensemble Machine Learning Approaches for Robust Classification of Maize Plant Leaf Diseases. Journal of Modern Technology, 87-93.
Hameed, R. T., Dheyab, S. A., Salman, S. A., Ali, A. H., & Mohamad, O. A. (2024). Secure Healthcare Systems and Big Data: A Bibliometrics Analysis. Iraqi Journal for Computer Science and Mathematics, 5(4), 22. DOI: https://doi.org/10.52866/2788-7421.1225
Qasim, S. S. (2024). IoT of Healthcare Innovation Solutions for Predictable Virus Detection. Babylonian Journal of Internet of Things, 2024, 126-136. DOI: https://orcid.org/0009-0005-4300-5105
Shin, J. (2024). Revolutionizing Medical Imaging with Artificial inelegant Real-Time Segmentation for Enhanced Diagnostics. EDRAAK, 2024, 18-25. DOI: https://orcid.org/0000-0002-7476-2468
Günoğlu, K., & AKKURT, İskender. (2023). Gamma-ray attenuation properties carbide compounds (WC, Mo2C, TiC, SiC, B4C) using Phy-X/PSD software. International Journal of Applied Sciences and Radiation Research, 1(1), 1–8. Retrieved from https://ijasrar.com/index.php/ijasrar/article/view/6
S. Esakkiammal, & K. Kasturi. (2024). Advancing Educational Outcomes with Artificial Intelligence: Challenges, Opportunities, And Future Directions. International Journal of Computational and Experimental Science and Engineering, 10(4);1749-1756. https://doi.org/10.22399/ijcesen.799
J. Prakash, R. Swathiramya, G. Balambigai, R. Menaha, & J.S. Abhirami. (2024). AI-Driven Real-Time Feedback System for Enhanced Student Support: Leveraging Sentiment Analysis and Machine Learning Algorithms. International Journal of Computational and Experimental Science and Engineering, 10(4);1567-1574. https://doi.org/10.22399/ijcesen.780
MOHAMMED, H. A., Adem, Şevki, & SAHAB, K. S. (2024). Estimation the Biochemical parameters Changes in Blood of Corona Virus Patients in Iraq in Order to Support the Timely Decision Needs . International Journal of Applied Sciences and Radiation Research, 1(1). Retrieved from https://ijasrar.com/index.php/ijasrar/article/view/4
M, V., V, J., K, A., Kalakoti, G., & Nithila, E. (2024). Explainable AI for Transparent MRI Segmentation: Deep Learning and Visual Attribution in Clinical Decision Support. International Journal of Computational and Experimental Science and Engineering, 10(4);575-584. https://doi.org/10.22399/ijcesen.479
Özlen, M. S., Cuma, A. B., Yazıcı, S. D., Yeğin, N., Demir, Özge, Aksoy, H., … Günay, O. (2024). Determination of Radiation Dose Level Exposed to Thyroid in C-Arm Scopy. International Journal of Applied Sciences and Radiation Research, 1(1). Retrieved from https://ijasrar.com/index.php/ijasrar/article/view/1
ÖZNACAR, T., & ERGENE, N. (2024). A Machine Learning Approach to Early Detection and Malignancy Prediction in Breast Cancer. International Journal of Computational and Experimental Science and Engineering, 10(4);911-917. https://doi.org/10.22399/ijcesen.516
J. Anandraj. (2024). Transforming Education with Industry 6.0: A Human-Centric Approach. International Journal of Computational and Experimental Science and Engineering, 10(4);1851-1862. https://doi.org/10.22399/ijcesen.732
ERTEKİN, R., RODOPLU, H., & GÜRSEL, S. (2024). The Use of Artificial Intelligence in Aviation: A Bibliometric Analysis. International Journal of Computational and Experimental Science and Engineering, 10(4);1863-1872. https://doi.org/10.22399/ijcesen.747
Türkmen, G., Sezen, A., & Şengül, G. (2024). Comparative Analysis of Programming Languages Utilized in Artificial Intelligence Applications: Features, Performance, and Suitability. International Journal of Computational and Experimental Science and Engineering, 10(3);461-469. https://doi.org/10.22399/ijcesen.342
Venkatraman Umbalacheri Ramasamy. (2024). Overview of Anomaly Detection Techniques across Different Domains: A Systematic Review. International Journal of Computational and Experimental Science and Engineering, 10(4);898-910. https://doi.org/10.22399/ijcesen.522
Jafar Ismail, R., Samar Jaafar Ismael, Dr. Sara Raouf Muhamad Amin, Wassan Adnan Hashim, & Israa Tahseen Ali. (2024). Survey of Multiple Destination Route Discovery Protocols. International Journal of Computational and Experimental Science and Engineering, 10(3);420-426. https://doi.org/10.22399/ijcesen.385
guven, mesut. (2024). Dynamic Malware Analysis Using a Sandbox Environment, Network Traffic Logs, and Artificial Intelligence. International Journal of Computational and Experimental Science and Engineering, 10(3);480-490. https://doi.org/10.22399/ijcesen.460
Serap ÇATLI DİNÇ, AKMANSU, M., BORA, H., ÜÇGÜL, A., ÇETİN, B. E., ERPOLAT, P., … ŞENTÜRK, E. (2024). Evaluation of a Clinical Acceptability of Deep Learning-Based Autocontouring: An Example of The Use of Artificial Intelligence in Prostate Radiotherapy. International Journal of Computational and Experimental Science and Engineering, 10(4);1181-1186. https://doi.org/10.22399/ijcesen.386
MOHAMMED, H. A., Adem, Şevki, & SAHAB, K. S. (2023). Optimal Examination Ways to follow up patients effected by COVID-19: case study in Jalawla General Hospital in Iraq. International Journal of Applied Sciences and Radiation Research, 1(1). Retrieved from https://ijasrar.com/index.php/ijasrar/article/view/5
Iskender Akkurt, Parisa Boodaghi Malidarreh & Roya Boodaghi Malidarre (2021): Simulation and Prediction the Attenuation Behavior of the KNN-LMN Based Lead Free Ceramics by FLUKA Code and Artificial Neural Network (ANN) - Based Algorithm, Environmental Technology, DOI: 10.1080/09593330.2021.2008017
Basyigit, C, Akkurt I, Kilincarslan S, Beycioglu A. Prediction of compressive strength of heavyweight concrete by ANN and FL models. NEURAL COMPUTING & APPLICATIONS 19-4(2010)507-513 DOI: 10.1007/s00521-009-0292-9
Akkurt, I Basyigit C., Kilincarslan S., Beycioglu, A. Prediction of photon attenuation coefficients of heavy concrete by fuzzy logic. JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS. 347-9 (2010)1589-1597. DOI: 10.1016/j.jfranklin.2010.06.002
Nuthakki, praveena, & Pavankumar T. (2024). Comparative Assessment of Machine Learning Algorithms for Effective Diabetes Prediction and Care. International Journal of Computational and Experimental Science and Engineering, 10(4)1337-1343. https://doi.org/10.22399/ijcesen.606
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 International Journal of Computational and Experimental Science and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
