• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2024, Volume: 17, Issue: 4, Pages: 317-324

Original Article

Hybrid Feature Selection for COVID-19 Severity Prediction Using Cuckoo Search with SVM Framework

Received Date:13 October 2023, Accepted Date:28 December 2023, Published Date:20 January 2024


Objective: The main objective of this study is to determine the most important blood test markers that may indicate the presence of COVID-19 in a patient. To utilize the Cuckoo Search algorithm with SVM to explore the feature space efficiently and select features that contribute significantly to the model's performance. Methods: A novel hybrid method for feature selection has been proposed with the goal of improving the predictive capabilities of Support Vector Machines (SVM) for determining COVID-19 severity. Blood test datasets are used in the implementation of this study. The dataset has been split into two parts: 80% for training and 20% for testing. First, we use two statistical measures, chi-squared and mutual information, from the filter approach to minimize the feature dimensions. As a wrapper for SVM, we then use a modified Cuckoo Search algorithm. To measure how well the proposed approach works, we used evaluation metrics such as accuracy, precision, recall, and F1 score. Findings: The SVM classifier achieved the best performance with the features obtained from the proposed hybrid method, and the SVM classifier obtained an accuracy of 92% using the blood test dataset. The outcomes demonstrate that our hybrid approach effectively picks a subset of features that makes the model simpler while also making it more accurate and faster to compute. Novelty: This research work proposes a new hybrid feature selection technique by combining filter and wrapper methods to find the best feature set. This combination is introduced for the first time in this type of work related to COVID-19 prediction in which the results of Chi-Square and Mutual Information are used by the modified Cuckoo-Search algorithm to find the top features pertaining to COVID-19 severity and also to improve the performance of SVM model.

Keywords: Feature selection, Cuckoo search, Machine learning, Support Vector Machine (SVM), Severity prediction, Healthcare


  1. Meraihi Y, Gabis AB, Mirjalili S, Ramdane-Cherif A, Alsaadi FE. Machine Learning-Based Research for COVID-19 Detection, Diagnosis, and Prediction: A Survey. SN Computer Science. 2022;3(4):1–35. Available from: https://doi.org/10.1007/s42979-022-01184-z
  2. Alballa N, Al-Turaiki I. Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review. Informatics in Medicine Unlocked. 2021;24:1–17. Available from: https://doi.org/10.1016/j.imu.2021.100564
  3. Zhou K, Sun Y, Li L, Zang Z, Wang J, Li J, et al. Eleven routine clinical features predict COVID-19 severity uncovered by machine learning of longitudinal measurements. Computational and Structural Biotechnology Journal. 2021;19:3640–3649. Available from: https://doi.org/10.1016/j.csbj.2021.06.022
  4. Singh V, Poonia RC, Kumar S, Dass P, Agarwal P, Bhatnagar V, et al. Prediction of COVID-19 corona virus pandemic based on time series data using support vector machine. Journal of Discrete Mathematical Sciences and Cryptography. 2020;23(8):1583–1597. Available from: https://doi.org/10.1080/09720529.2020.1784535
  5. Bao FS, He Y, Liu J, Chen Y, Li Q, Zhang CR, et al. Triaging moderate COVID-19 and other viral pneumonias from routine blood tests. 2020. Available from: https://doi.org/10.48550/arXiv.2005.06546
  6. Batista AFdM, Miraglia JL, Donato THR, Filho ADPC. COVID-19 diagnosis prediction in emergency care patients: a machine learning approach. MedRxiv. 2020;p. 1–8. Available from: https://www.medrxiv.org/content/10.1101/2020.04.04.20052092v2.full.pdf
  7. Shoer S, Karady T, Keshet A, Shilo S, Rossman H, Gavrieli A, et al. A Prediction Model to Prioritize Individuals for a SARS-CoV-2 Test Built from National Symptom Surveys. Med. 2021;2(2):196–208. Available from: https://doi.org/10.1016/j.medj.2020.10.002
  8. Zhang J, Xiong Y, Min S. A new hybrid filter/wrapper algorithm for feature selection in classification. Analytica Chimica Acta. 2019;1080:43–54. Available from: https://doi.org/10.1016/j.aca.2019.06.054
  9. Singh N, Singh P. A hybrid ensemble-filter wrapper feature selection approach for medical data classification. Chemometrics and Intelligent Laboratory Systems. 2021;217:104396. Available from: https://doi.org/10.1016/j.chemolab.2021.104396
  10. Khan A, Khan A, Bangash JI, Subhan F, Khan A, Khan A, et al. Cuckoo Search-based SVM (CS-SVM) Model for Real-Time Indoor Position Estimation in IoT Networks. Security and Communication Networks. 2021;2021:1–7. Available from: https://doi.org/10.1155/2021/6654926
  11. Brinati D, Campagner A, Ferrari D, Locatelli M, Banfi G, Cabitza F. Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study. Journal of Medical Systems. 2020;44(8):1–12. Available from: https://doi.org/10.1007/s10916-020-01597-4
  12. Karrar AE. The Effect of Using Data Pre-Processing by Imputations in Handling Missing Values. Indonesian Journal of Electrical Engineering and Informatics (IJEEI). 2022;10(2):375–384. Available from: http://section.iaesonline.com/index.php/IJEEI/article/viewFile/3730/697
  13. Ahmad HF, Khaloofi H, Azhar Z, Algosaibi A, Hussain J. An Improved COVID-19 Forecasting by Infectious Disease Modelling Using Machine Learning. Applied Sciences. 2021;11(23):1–38. Available from: https://doi.org/10.3390/app112311426
  14. Chauhan VK, Dahiya K, Sharma A. Problem formulations and solvers in linear SVM: a review. Artificial Intelligence Review. 2019;52(2):803–855. Available from: https://doi.org/10.1007/s10462-018-9614-6
  15. Chen S, Webb GI, Liu L, Ma X. A novel selective naïve Bayes algorithm. Knowledge-Based Systems. 2020;192:105361. Available from: https://doi.org/10.1016/j.knosys.2019.105361
  16. Nusinovici S, Tham YC, Yan MYC, Ting DSW, Li J, Sabanayagam C, et al. Logistic regression was as good as machine learning for predicting major chronic diseases. Journal of Clinical Epidemiology. 2020;122:56–69. Available from: https://doi.org/10.1016/j.jclinepi.2020.03.002
  17. Cabitza F, Campagner A, Ferrari D, Resta CD, Ceriotti D, Sabetta E, et al. Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests. Clinical Chemistry and Laboratory Medicine (CCLM). 2020;59(2):421–431. Available from: https://doi.org/10.1515/cclm-2020-1294
  18. Hany N, Atef N, Mostafa N, Mohamed S, Elsahhar M, Abdelraouf A. Detection COVID-19 using Machine Learning from Blood Tests. In: 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC). (pp. 229-234) IEEE. 2021.


© 2024 Priya & Rajeswari. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


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