Transformative approach in Lassa fever diagnostics: an innovative integrative strategy for early detection and outcome prediction

Authors

  • Denis U. Ashishie
    Department of Computer Science, University of Calabar PMB 1115, Eta 540004, Etta Agbo Rd, Calabar, Nigeria
  • Endurance O. Obi
    Department of Computer Science, University of Calabar PMB 1115, Eta 540004, Etta Agbo Rd, Calabar, Nigeria
  • Osowomuabe Njama-Abang
    Department of Computer Science, University of Calabar PMB 1115, Eta 540004, Etta Agbo Rd, Calabar, Nigeria
  • Ahena I. Bassey
    Department of Computer Science, University of Calabar PMB 1115, Eta 540004, Etta Agbo Rd, Calabar, Nigeria

Keywords:

Lassa fever, Hybrid machine learning, Diagnostic models, Risk prediction

Abstract

This article has been retracted by the Editor-in-Chief of African Scientific Reports following post-publication concerns regarding the accuracy and appropriateness of several cited references and their correspondence to the statements for which they were cited, particularly in relation to methodological and disease-specific claims.

In line with standard editorial practice, the authors were invited to review these issues and to submit a corrected version of the manuscript to ensure that all citations accurately and appropriately supported the text. Unfortunately, the requested corrections could not be completed.

To ensure the accuracy and reliability of the scholarly record, and in accordance with the journal’s editorial policies and the Committee on Publication Ethics (COPE) guidelines, the Editor-in-Chief, on behalf of the Editorial Board, has therefore decided to retract this article.

This action is taken solely to maintain the integrity of the published record. No determination has been made regarding the intent of the authors.

The article will remain accessible for reference purposes but will be clearly marked as retracted.

Dimensions

[1] W. B. Yahya, Y. Bello & A. AbdulRaheem, “Model fitness and predictive accuracy in linear mixed-effects models with latent cluster”, Journal of the Nigerian Society of Physical Sciences 5 (2023) 1437. https://doi.org/10.46481/jnsps.2023.1437.

[2] Z.-H. Zhou, J. Wu & W. Tang, “Ensembling neural networks: many could be better than all”, Artificial Intelligence 137 (2002) 239. https://doi.org/10.1016/S0004-3702(02)00190-X.

[3] I. Kononenko, “Machine learning for medical diagnosis: history, state of the art and perspective”, Artificial Intelligence in Medicine 23 (2001) 89. https://doi.org/10.1016/S0933-3657(01)00077-X.

[4] T. Chen & C. Guestrin, “XGBoost: A scalable tree boosting system”, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 2016, pp. 785–794. https://doi.org/10.1145/2939672.2939785.

[5] J. H. Friedman, “Greedy function approximation: a gradient boosting machine”, The Annals of Statistics 29 (2001) 1189. https://doi.org/10.1214/aos/1013203451.

[6] T. Dietterich, “Ensemble methods in machine learning”, Multiple Classifier Systems, Lecture Notes in Computer Science 1857 (2000) 1. https://doi.org/10.1007/3-540-45014-9_1.

[7] L. Breiman, “Random forests”, Machine Learning 45 (2001) 5. https://doi.org/10.1023/A:1010933404324.

[8] G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye & T.-Y. Liu, “LightGBM: a highly efficient gradient boosting decision tree”, Advances in Neural Information Processing Systems 30 (2017) 3146. https://doi.org/10.5555/3294996.3295074.

[9] N. Cristianini & J. Shawe-Taylor, An Introduction to support vector machines and other kernel-based learning methods, Cambridge University Press, Cambridge, United Kingdom, 2000, pp. 1–189. https://doi.org/10.1017/CBO9780511801389.

[10] D. J. Hand & R. J. Till, “A simple generalisation of the area under the ROC curve for multiple class classification problems”, Machine Learning 45 (2001) 171. https://doi.org/10.1023/A:1010920819831.

[11] H. He & E. A. Garcia, “Learning from imbalanced data”, IEEE Transactions on Knowledge and Data Engineering 21 (2009) 1263. https://doi.org/10.1109/TKDE.2008.239.

[12] N. V. Chawla, K.W. Bowyer, L. O. Hall &W. P. Kegelmeyer, “SMOTE: Synthetic minority over-sampling technique”, Journal of Artificial Intelligence Research 16 (2002) 321. https://doi.org/10.1613/jair.953.

[13] X.-Y. Liu, J. Wu & Z.-H. Zhou, “Exploratory undersampling for class-imbalance learning”, IEEE Transactions on Systems, Man, and Cybernetics, Part B 39 (2009) 539. https://doi.org/10.1109/TSMCB.2008.2007853.

[14] Z. Wang, C. Yan & Y. Cho, “Machine learning for medical applications: an overview of ensemble methods”, IEEE Access 7 (2019) 6350. https://doi.org/10.1109/ACCESS.2019.2963279.

[15] M. Galar, A. Fernandez, E. Barrenechea, H. Bustince & F. Herrera, “A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybridbased approaches”, IEEE Transactions on Systems, Man, and Cybernetics, Part C 42 (2012) 463. https://doi.org/10.1109/TSMCC.2011.2161285.

[16] Y. Sun, A. K. C.Wong & M. S. Kamel, “Classification of imbalanced data: a review”, International Journal of Pattern Recognition and Artificial Intelligence 23 (2009) 687. https://doi.org/10.1142/S0218001409007326.

[17] S. Wang & X. Yao, “Diversity analysis on imbalanced data sets by using ensemble models”, IEEE Symposium on Computational Intelligence and Data Mining (2009) 324. https://doi.org/10.1109/CIDM.2009.4938667.

[18] B. Krawczyk, M. Wo´zniak & G. Schaefer, “Cost-sensitive decision tree ensembles for effective imbalanced classification”, Applied Soft Computing 14 (2014) 554. https://doi.org/10.1016/j.asoc.2013.08.014.

[19] F. O. Fasina, A. O. Shittu, D. Lazarus, O. T. Tomori, L. Simonsen, C. Viboud & G. Chowell, “Transmission dynamics and control of Ebola virus disease outbreak in Nigeria, July to September 2014”, Eurosurveillance 19 (2014) 20920. https://doi.org/10.2807/1560-7917.ES2014.19.40.20920.

[20] A. L. Blum & P. Langley, “Selection of relevant features and examples in machine learning,” Artificial Intelligence 97 (1997) 245. https://doi.org/10.1016/S0004-3702(97)00063-5.

[21] J. Han, M. Kamber & J. Pei, Data Mining: concepts and techniques, Morgan Kaufmann, Burlington, Massachusetts, USA, 2011, pp. 1–744. https://ia800603.us.archive.org/2/items/datamining_201811/DS-book%20u5.pdf.

[22] T. Fawcett, “An introduction to ROC analysis”, Pattern Recognition Letters 27 (2006) 861. https://doi.org/10.1016/j.patrec.2005.10.010

[23] J. Demsar, “Statistical comparisons of classifiers over multiple data sets” Journal of Machine Learning Research 7 (2006) 1. https://www.jmlr.org/papers/volume7/demsar06a/demsar06a.pdf.

[24] NCDC, “An update of Lassa fever outbreak in Nigeria”, Ncdc.gov.ng, 2019. https://ncdc.gov.ng/diseases/sitreps/?cat=5&name=An%20update%20of%20Lassa%20fever%20outbreak%20in%20Nigeria.

[25] E. O. Obi, “Development of a hybrid machine learning model for the detection of patients infected with Lassa fever”, 2024. https://deepnote.com/workspace/Obi-aba3a7aa-56c0-4a7f-8a84-2a3f07cb4d01/project/Lassa-034bbfb7-60f4-4f8d-b40e-295e3ceb17a8/notebook/20241127-045451-Notebook-1-2-e8fc3de7aa26493f928d013dcb108e9d?utm_source=share-modal&utm_medium=product-shared-content&utm_campaign=notebook&utm_content=034bbfb7-06f4-4f8d-b40e-295e3ceb17a8.

Published

2025-10-28

How to Cite

Transformative approach in Lassa fever diagnostics: an innovative integrative strategy for early detection and outcome prediction. (2025). African Scientific Reports, 4(3), 338. https://doi.org/10.46481/asr.2025.4.3.338

Issue

Volume 4, Issue 3, December 2025

Section

MATHEMATICAL SCIENCES SECTION
Copyright & Licensing

Copyright (c) 2025 Denis U. Ashishie, Endurance O. Obi, Osowomuabe Njama-Abang, Ahena I. Bassey

Creative Commons License

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

How to Cite

Transformative approach in Lassa fever diagnostics: an innovative integrative strategy for early detection and outcome prediction. (2025). African Scientific Reports, 4(3), 338. https://doi.org/10.46481/asr.2025.4.3.338

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