Chronic Diseases Prediction on COVID-19 Patients Using Machine Learning Techniques

Aurko, Abeed Hanif; Abid, S. M. Fahim; Mannan, Dewan Tarikul

dc.contributor.author	Aurko, Abeed Hanif
dc.contributor.author	Abid, S. M. Fahim
dc.contributor.author	Mannan, Dewan Tarikul
dc.date.accessioned	2022-04-08T02:49:54Z
dc.date.available	2022-04-08T02:49:54Z
dc.date.issued	2021-03-30
dc.identifier.citation	[1] Saba Bashir, Usman Qamar, and Farhan Hassan Khan. Intellihealth: a med- ical decision support application using a novel weighted multi-layer classi er ensemble framework. Journal of biomedical informatics, 59:185{200, 2016. [2] Zhichao Feng, Qizhi Yu, Shanhu Yao, Lei Luo, Wenming Zhou, Xiaowen Mao, Jennifer Li, Junhong Duan, Zhimin Yan, Min Yang, et al. Early prediction of disease progression in covid-19 pneumonia patients with chest ct and clinical characteristics. Nature communications, 11(1):1{9, 2020. [3] Jad Gerges Harb, Hussein A Noureldine, Georges Chedid, Mariam Nour El- dine, Dany Abou Abdallah, Nancy Falco Chedid, and Wared Nour-Eldine. Sars, mers and covid-19: clinical manifestations and organ-system complica- tions: a mini review. Pathogens and Disease, 78(4):ftaa033, 2020. [4] Elisa Grifoni, Alice Valoriani, Francesco Cei, Vieri Vannucchi, Federico Mo- roni, Lorenzo Pelagatti, Roberto Tarquini, Giancarlo Landini, and Luca Ma- sotti. The call score for predicting outcomes in patients with covid-19. Clinical Infectious Diseases, 72(1):182{183, 2021. [5] Yan Han, Chongyan Chen, Ahmed H Tew k, Ying Ding, and Yifan Peng. Pneumonia detection on chest x-ray using radiomic features and contrastive learning. arXiv preprint arXiv:2101.04269, 2021. [6] Sumeyye Kazancioglu, Aliye Bastug, Bahadir Orkun Ozbay, Nizamettin Kemirtlek, and Hurrem Bodur. The role of haematological parameters in patients with covid-19 and in uenza virus infection. Epidemiology & Infec- tion, 148, 2020. [7] Wassim W Labaki, Carlos H Martinez, Fernando J Martinez, Craig J Galb an, Brian D Ross, George R Washko, R Graham Barr, Elizabeth A Regan, Har- 30 vey O Coxson, Eric A Ho man, et al. The role of chest computed tomography in the evaluation and management of the patient with chronic obstructive pul- monary disease. American journal of respiratory and critical care medicine, 196(11):1372{1379, 2017. [8] Senthilkumar Mohan, Chandrasegar Thirumalai, and Gautam Srivastava. Ef- fective heart disease prediction using hybrid machine learning techniques. IEEE Access, 7:81542{81554, 2019. [9] S Muthuselvan, S Rajapraksh, K Somasundaram, and K Karthik. Classi ca- tion of liver patient dataset using machine learning algorithms. International Journal of Engineering & Technology, 7(3.34):323{326, 2018. [10] Helena Nyblom, Ulf Berggren, Jan Balldin, and Rolf Olsson. High ast/alt ratio may indicate advanced alcoholic liver disease rather than heavy drinking. Alcohol and alcoholism, 39(4):336{339, 2004. [11] World Health Organization. Coronavirus disease (covid-19) pandemic. https://www.who.int/emergencies/diseases/novel-coronavirus-2019, 2021. [12] Nenad Petrovi c. Simulation environment for optimal resource planning during covid-19 crisis. In 2020 55th International Scienti c Conference on Informa- tion, Communication and Energy Systems and Technologies (ICEST), pages 23{26. IEEE, 2020. [13] UCI Machine Learning Repository. Bupa liver disease dataset. https://archive.ics.uci.edu/ml/datasets/liver+disorders. [14] UCI Machine Learning Repository. Cleveland heart disease data set. https://archive.ics.uci.edu/ml/datasets/heart+disease. [15] UCI Machine Learning Repository. Indian liver patient dataset. https://archive.ics.uci.edu/ml/datasets/ILPD+(Indian+Liver+Patient+Dataset). [16] UCI Machine Learning Repository. Spect heart data set. https://archive.ics.uci.edu/ml/datasets/spect+heart. 31 [17] UCI Machine Learning Repository. Statlog (heart) data set. http://archive.ics.uci.edu/ml/datasets/statlog+(heart). [18] Amy G Shah, Alison Lydecker, Karen Murray, Brent N Tetri, Melissa J Con- tos, Arun J Sanyal, Nash Clinical Research Network, et al. Comparison of noninvasive markers of brosis in patients with nonalcoholic fatty liver dis- ease. Clinical gastroenterology and hepatology, 7(10):1104{1112, 2009. [19] Amit Kumar Shrivastava, Harsh Vardhan Singh, Arun Raizada, and San- jeev Kumar Singh. C-reactive protein, in ammation and coronary heart dis- ease. The Egyptian Heart Journal, 67(2):89{97, 2015. [20] Marjia Sultana, Afrin Haider, and Mohammad Shorif Uddin. Analysis of data mining techniques for heart disease prediction. In 2016 3rd international con- ference on electrical engineering and information communication technology (ICEEICT), pages 1{5. IEEE, 2016. [21] Abhishek Taneja et al. Heart disease prediction system using data mining techniques. Oriental Journal of Computer science and technology, 6(4):457{ 466, 2013. [22] Liam Townsend, Adam H Dyer, Karen Jones, Jean Dunne, Aoife Mooney, Fiona Ga ney, Laura O'Connor, Deirdre Leavy, Kate O'Brien, Joanne Dowds, et al. Persistent fatigue following sars-cov-2 infection is common and inde- pendent of severity of initial infection. Plos one, 15(11):e0240784, 2020. [23] Clyde W Yancy and Gregg C Fonarow. Coronavirus disease 2019 (covid- 19) and the heart\|is heart failure the next chapter? JAMA cardiology, 5(11):1216{1217, 2020. [24] Li Zuo, Ying-Chun Ma, Yu-Hong Zhou, Mei Wang, Guo-Bin Xu, and Hai- Yan Wang. Application of gfr-estimating equations in chinese patients with chronic kidney disease. American journal of kidney diseases, 45(3):463{472, 2005.	en_US
dc.identifier.uri	http://hdl.handle.net/123456789/1309
dc.description	Supervised by Mr. Md. Hamjajul Ashmafee, Lecturer, Department of Computer Science and Engineering (CSE) Islamic University of Technology (IUT) Gazipur, Bangladesh	en_US
dc.description.abstract	COVID - 19 pandemic has spread to more than 210 countries. Millions of people lost their lives due to COVID - 19. COVID-19 death rate is almost 2.2%. So, the majority of people are recovering from the disease. But recently a lot of peo- ple recovered from COVID 19 are developing chronic diseases (ie. Heart failure, Stroke, Chronic Kidney disease, Liver damage, Chronic Obstructive Pulmonary disease, Shock, Blood Clotting etc). which is really alarming. In our work we tried to develop a combine system to predict the after COVID - 19 chronic dis- ease probability. Here we rst developed a central model which works well for di erent individual disease predictions (heart, lungs, kidney, liver diseases). Then we worked with COVID - 19 patients data to predict the probability of chronic diseases based on the changes in di erent haematological parameters. It will help the COVID - 19 recovered patients to take precautionary measures against chronic diseases to minimize the diseases and avoid the casualties.	en_US
dc.language.iso	en	en_US
dc.publisher	Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur-1704, Bangladesh	en_US
dc.title	Chronic Diseases Prediction on COVID-19 Patients Using Machine Learning Techniques	en_US
dc.type	Thesis	en_US