Cloud-Based Realtime Decision System for Severity Classification of COVID-19 Self-Isolation Patients using Machine Learning Algorithm


Bhima Satria Rizki Sugiono(1); Mokh. Sholihul Hadi(2*); Ilham Ari Elbaith Zaeni(3); Sujito Sujito(4); Mhd Irvan(5);

(1) Universitas Negeri Malang
(2) Universitas Negeri Malang
(3) Universitas Negeri Malang
(4) Universitas Negeri Malang
(5) The University of Tokyo
(*) Corresponding Author

  

Abstract


The global impact of the COVID-19 pandemic has been profound, affecting economies and societal structures worldwide. Indonesia, with a high caseload, has encountered significant challenges across various sectors. Virus transmission primarily occurs through physical contact, and the surge in active cases has strained hospital capacities, leading to the hospitalization of only severe cases. The remaining patients receive home telecare, but some experience sudden health deterioration with fatal consequences. To address this issue, this study proposes a remote outpatient care system utilizing Internet of Things (IoT) technology and medical electronics. This integrated system aims to provide an effective response to the COVID-19 pandemic. The research includes a comparative analysis of three machine-learning algorithms: decision tree, gradient tree boosting, and random forest for the classification of COVID-19 patients. The results reveal that the random forest algorithm outperforms the others with an accuracy rate of 70%, as compared to 67% for the decision tree and 62% for the gradient tree boosting algorithm. This integrated system not only addresses immediate healthcare delivery challenges but also offers data-driven insights for patient classification, thereby enhancing the effectiveness and reach of medical interventions


Keywords


Decision Tree; Gradient Tree Boosting; Random Forest; Realtime Decision

  
  

Full Text:

PDF
  

Article Metrics

Abstract view: 468 times
PDF view: 103 times 		     

Digital Object Identifier

doi  https://doi.org/10.33096/ilkom.v15i3.1945.413-426 		  

Cite

How to cite item

References


M. B. Jamshidi, A. Lalbakhsh, J. Talla, Z. Peroutka, F. Hadjilooei, P. Lalbakhsh, et al., "Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment," IEEE Access, vol. 1, pp. 1-1, 2020.

Q. Li, X. Guan, P. Wu, X. Wang, L. Zhou, Y. Tong, et al., "Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia," New England journal of medicine, vol. 1, pp. 1-1, 2020.

3 V. K. Gupta, A. Gupta, D. Kumar, A. Sardana, "Prediction of COVID-19 confirmed, death, and cured cases in India using random forest model," Big Data Mining and Analytics, vol. 4, no. 2, pp. 116-23, Feb. 2021.

4 V. A. D. F. Barbosa, J. C. Gomes, M. A. de Santana, C. L. de Lima, R. B. Calado, C. R. Bertoldo Junior, et al., "Covid-19 rapid test by combining a random forest-based web system and blood tests," Journal of Biomolecular Structure and Dynamics, vol. 1, pp. 1-20, 2021.

S. Anggraini, M. Akbar, A. Wijaya, H. Syaputra, M. Sobri, "Classification of Symptoms of Coronavirus Disease 19 (COVID-19) Using Machine Learning," Journal of Software Engineering Ampera, vol. 2, no. 1, pp. 57-68, 2021.

B. A. Goldstein, E. C. Polley, F. B. Briggs, "Random forests for genetic association studies," Stat Appl Genet Mol Biol., vol. 10, no. 1, pp. 32, July 2011.

V. Svetnik, A. Liaw, C. Tong, J. C. Culberson, R. P. Sheridan, B. P. Feuston, "Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling," Journal of Chemical Information and Computer Sciences, vol. 43, no. 6, pp. 1947–1958, 2003.

L. Alhusain, A. M. Hafez, "Cluster ensemble based on Random Forests for genetic data," BioData Mining, vol. 10, no. 1, 2017.

D. Yuan, J. Huang, X. Yang, J. Cui, "Improved random forest classification approach based on hybrid clustering selection," 2020 Chinese Automation Congress (CAC), 2020.

C. Zhan, Y. Zheng, H. Zhang, Q. Wen, "Random-forest-Bagging broad learning system with applications for COVID-19 pandemic," IEEE Internet of Things Journal, vol. 8, no. 21, pp. 15906-15918, 2021.

H. Lan and Y. Pan, "A Crowdsourcing Quality Prediction Model Based on Random Forests," in Proc. IEEE/ACIS 18th Int. Conf. on Computer and Information Science (ICIS), 2019, pp. 315-319.

V. Jackins, S. Vimal, M. Kaliappan, and M.Y. Lee, "AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes," The Journal of Supercomputing, vol. 77, no. 5, pp. 5198-5219, 2021.

M. Hemalatha, "A hybrid random forest deep learning classifier empowered edge cloud architecture for COVID-19 and pneumonia detection," Expert Systems with Applications, vol. 210, p. 118227, 2022.

A.D. Salman, H.A.D. AL-farttoosi, and A.J. Kadhim, "Study impact of the latitude on Covid-19 spread virus by data mining algorithm," in Journal of Physics: Conference Series, vol. 1664, no. 1, p. 012109, Nov. 2020.

15 S. Yamasaki, K. Yaji, and K. Fujita, "Knowledge discovery in databases for determining formulation in topology optimization," Structural and Multidisciplinary Optimization, vol. 59, no. 2, pp. 595-611, 2019.

A. Linsel, K. Bär, J. Haas, J. Hornung, M.D. Greb, and M. Hinderer, "GeoReVi: A knowledge discovery and data management tool for subsurface characterization," SoftwareX, vol. 12, p. 100597, 2020.

V.R. Sari, F. Firdausi, and Y. Azhar, "Comparison of Arabica Coffee Quality Predictions using SGD, Random Forest and Naive Bayes Algorithms," Edumatic: Journal of Informatics Education, vol. 4, no. 2, pp. 1-9, Dec. 2020.

A. Assegaf, M.A. Mukid, and A. Hoyyi, "Bank Health Analysis Using Local Mean K-Nearest Neighbor and Multi Local Means K-Harmonic Nearest Neighbor," Gaussian Journal, vol. 8, no. 3, pp. 343-5, Aug. 30, 2019.

F.S. Pamungkas, B.D. Prasetya, and I. Kharisudin, "Comparison of Supervised Learning Classification Methods on Data Bank Customers Using Python," in PRISMA, Proc. of the 2020 National Seminar on Mathematics, Mar. 4, vol. 3, pp. 692-697.

H. Liu and M. Cocea, "Semi-random partitioning of data into training and test sets in granular computing context," Granular Computing, vol. 2, no. 4, pp. 357-386, 2017.

J. Zhao, Y. Zhang, X. He, and P. Xie, "Covid-ct-dataset: a ct scan dataset about covid-19," arXiv preprint arXiv:2003.13865, 2020.

G. Varma, A. Subramanian, A. Namboodiri, M. Chandraker, and C.V. Jawahar, "IDD: A dataset for exploring problems of autonomous navigation in unconstrained environments," in Proc. IEEE Winter Conf. on Applications of Computer Vision (WACV), 2019, pp. 1743-1751.

M.M. Islam, F. Karray, R. Alhajj, and J. Zeng, "A review on deep learning techniques for the diagnosis of novel coronavirus (COVID-19)," IEEE Access, vol. 9, pp. 30551-30572, 2021.

K.H. Abdulkareem, M.A. Mohammed, A. Salim, M. Arif, O. Geman, D. Gupta, and A. Khanna, "Realizing an effective COVID-19 diagnosis system based on machine learning and IOT in smart hospital environment," IEEE Internet of Things Journal, vol. 8, no. 21, pp. 15919-15928, 2021.

E.P. Doheny, M. Flood, S. Ryan, C. McCarthy, O. O'Carroll, C. O'Seaghdha, P.W. Mallon, E.R. Feeney, V.M. Keatings, M. Wilson, and N. Kennedy, "Prediction of low pulse oxygen saturation in COVID-19 using remote monitoring post hospital discharge," International Journal of Medical Informatics, 2022, p. 104911.

W. Hou, Z. Zhao, A. Chen, H. Li, and T.Q. Duong, "Machining learning predicts the need for escalated care and mortality in COVID-19 patients from clinical variables," International Journal of Medical Sciences, vol. 18, no. 8, p. 1739, 2021.

D. Assaf, Y.A. Gutman, Y. Neuman, G. Segal, S. Amit, S. Gefen-Halevi, N. Shilo, A. Epstein, R. Mor-Cohen, A. Biber, and G. Rahav, "Utilization of machine-learning models to accurately predict the risk for critical COVID-19," Internal and Emergency Medicine, vol. 15, no. 8, pp. 1435-1443, 2020.

F. Tezza, G. Lorenzoni, D. Azzolina, S. Barbar, L.A.C. Leone, and D. Gregori, "Predicting in-hospital mortality of patients with COVID-19 using machine learning techniques," Journal of Personalized Medicine, vol. 11, no. 5, p. 343, 2021.

I. Markoulidakis, I. Rallis, I. Georgoulas, G. Kopsiaftis, A. Doulamis, and N. Doulamis, "Multiclass Confusion Matrix Reduction Method and Its Application on Net Promoter Score Classification Problem," Technologies, vol. 9, no. 4, p. 81, 2021.

M. Pourhomayoun and M. Shakibi, "Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making," Smart Health, vol. 20, p. 100178, 2021.

K. El Asnaoui and Y. Chawki, "Using X-ray images and deep learning for automated detection of coronavirus disease," Journal of Biomolecular Structure and Dynamics, vol. 39, no. 10, pp. 3615-3626, 2021.

G.Y. Kim, J.Y. Kim, C.H. Kim, and S.M. Kim, "Evaluation of deep learning for COVID-19 diagnosis: impact of image dataset organization," Journal of Applied Clinical Medical Physics, vol. 22, no. 7, pp. 297-305, 2021.

A. Pranolo and Y. Mao, "Cae-covidx: Automatic covid-19 disease detection based on x-ray images using enhanced deep convolutional and autoencoder," International Journal of Advances in Intelligent Informatics, vol. 7, no. 1, pp. 49-62, 2021.

M. Sevi and İ. Aydin, "COVID-19 detection using deep learning methods," in Proc. 2020 International conference on data analytics for business and industry: way towards a sustainable economy (ICDABI), 2020, pp. 1-6.

C. Mouradian, F. Ebrahimnezhad, Y. Jebbar, J.K. Ahluwalia, S.N. Afrasiabi, R.H. Glitho, and A. Moghe, "An IoT Platform-as-a-Service for NFV Based-Hybrid Cloud/Fog Systems," IEEE Internet of Things Journal, 2020, doi:10.1109/jiot.2020.2968235.

Z. Li, Y. Zhang, and Y. Liu, "Towards a full-stack devops environment (platform-as-a-service) for cloud-hosted applications," Tsinghua Science and Technology, vol. 22, no. 01, pp. 1-9, 2017.

S.J. Taylor, A. Anagnostou, T. Kiss, G. Terstyanszky, P. Kacsuk, N. Fantini, D. Lakehal, and J. Costes, "Enabling cloud-based computational fluid dynamics with a platform-as-a-service solution," IEEE Transactions on Industrial Informatics, vol. 15, no. 1, pp. 85-94, 2018.

P. Dauni, M.D. Firdaus, R. Asfariani, M.I.N. Saputra, A.A. Hidayat, and W.B. Zulfikar, "Implementation of Haversine formula for school location tracking," in Journal of Physics: Conference Series, vol. 1402, no. 7, p. 077028, IOP Publishing, 2019.

M.M. Khan, M.R. Amin, A. Al Mamun, and A.A. Sajib, "Development of Web Based Online Medicine Delivery System for COVID-19 Pandemic," Journal of Software Engineering and Applications, vol. 14, no. 1, pp. 26-43, 2021.

İ.B. Cicek, İ. Sel, F.H. YAĞIN, and C. Colak, "Development of a Python-Based Classification Web Interface for Independent Datasets," Balkan Journal of Electrical and Computer Engineering, vol. 10, no. 1, pp. 91-96, 2021.

S.J. Sidiq and M. Zaman, "Using Unpruned Decision Tree and Random Forest For Learning From Multi-Class Imbalanced Data," Think India Journal, vol. 22, no. 30, pp. 758-763, 2019.

A. Ishaq, S. Sadiq, M. Umer, S. Ullah, S. Mirjalili, V. Rupapara, and M. Nappi, "Improving the prediction of heart failure patients’ survival using SMOTE and effective data mining techniques," IEEE Access, vol. 9, pp. 39707-39716, 2021.

A. Fernández, S. Garcia, F. Herrera, and N.V. Chawla, "SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary," Journal of Artificial Intelligence Research, vol. 61, pp. 863-905, 2018.

A. Ishaq, S. Sadiq, M. Umer, S. Ullah, S. Mirjalili, V. Rupapara, and M. Nappi, "Improving the prediction of heart failure patients’ survival using SMOTE and effective data mining techniques," IEEE Access, vol. 9, pp. 39707-39716, 2021.

M. Mukherjee and M. Khushi, "SMOTE-ENC: A novel SMOTE-based method to generate synthetic data for nominal and continuous features," Applied System Innovation, vol. 4, no. 1, p. 18, 2021.

Z. Xu, D. Shen, T. Nie, and Y. Kou, "A hybrid sampling algorithm combining M-SMOTE and ENN based on random forest for medical imbalanced data," Journal of Biomedical Informatics, vol. 107, p. 103465, 2020.

X. Tan, S. Su, Z. Huang, X. Guo, Z. Zuo, X. Sun, and L. Li, "Wireless sensor networks intrusion detection based on SMOTE and the random forest algorithm," Sensors, vol. 19, no. 1, p. 203, 2019.

S.F. Abdoh, M.A. Rizka, and F.A. Maghraby, "Cervical cancer diagnosis using random forest classifier with SMOTE and feature reduction techniques," IEEE Access, vol. 6, pp. 59475-59485, 2018.

K. Shah, H. Patel, D. Sanghvi, and M. Shah, "A comparative analysis of logistic regression, random forest and KNN models for the text classification," Augmented Human Research, vol. 5, no. 1, pp. 1-16, 2020.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Bhima Satria Rizki Sugiono, Mokh. Sholihul Hadi, Ilham Ari Elbaith Zaeni, Sujito Sujito, Mhd Irvan

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