Prediction of Anemia using various Ensemble Learning and Boosting Techniques

Authors

  • Nalluri Shweta Vellore Institute of Technology University image/svg+xml
  • Sagar Dhanraj Pande Vellore Institute of Technology University image/svg+xml

DOI:

https://doi.org/10.4108/eetpht.9.4197

Keywords:

Anemia, Prediction, Machine Learning, Random Forest, Ensemble learning, Boosting

Abstract

INTRODUCTION: Anemia is a disease of great concern. It is mainly seen in people who are deficient in several vitamins like B12 and those who are deficient in iron. Neglecting the situation and leaving it untreated could lead to severe consequences in the future. Hence it is of great importance to predict Anemia in an individual and treat it in the optimum stage.

OBJECTIVES: In this paper, machine learning was used for the prediction of Anemia.

METHODS: The dataset used for this was formed by combining different datasets from Kaggle. The accuracy of various machine learning techniques was evaluated to find out the best one. Along with the supervised learning algorithms like Random Forest, SVM, Naive Bayes etc., Linear Discriminant Analysis, Quadratic Discriminant Analysis and ensemble learning methods were also performed.

RESULTS: Upon evaluation, among the best performers, the execution time was also taken into consideration to determine which classifier works well. Among all the algorithms used, XGboost worked the best with an optimum execution time.

CONCLUSION: The conclusion is that for the data used in the work, XGboost results as the best model.

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References

Kumar K, Kumar P, Deb D, Unguresan ML, Muresan V. Artificial Intelligence and Machine Learning Based Intervention in Medical Infrastructure: A Review and Future Trends. Healthcare (Basel). 2023 Jan 10;11(2):207 DOI: https://doi.org/10.3390/healthcare11020207

Dhakal, Prakriti & Khanal, Santosh & Bista, Rabindra. (2023). Prediction of Anemia using Machine Learning Algorithms. International Journal of Computer Science and Information Technology. 15. 15-30. 10.5121/ijcsit.2023.15102. DOI: https://doi.org/10.5121/ijcsit.2023.15102

Uddin, S., Khan, A., Hossain, M. E., & Moni, M. A. (2019). Comparing different supervised machine learning algorithms for disease prediction. BMC medical informatics and decision making, 19(1), 1-16. DOI: https://doi.org/10.1186/s12911-019-1004-8

Jaiswal, M., Srivastava, A., & Siddiqui, T. J. (2019). Machine learning algorithms for anemia disease prediction. In Recent Trends in Communication, Computing, and Electronics: Select Proceedings of IC3E 2018 (pp. 463-469). Springer Singapore. DOI: https://doi.org/10.1007/978-981-13-2685-1_44

Sarsam, S. M., Al-Samarraie, H., Alzahrani, A. I., & Shibghatullah, A. S. (2022). A non-invasive machine learning mechanism for early disease recognition on Twitter: The case of anemia. Artificial Intelligence in Medicine, 134, 102428. DOI: https://doi.org/10.1016/j.artmed.2022.102428

Geetha, V., Gomathy, C. K., Keerthi, K., & Pavithra, N. (2022). Diagnostic Approach To Anemia In Adults Using Machine Learning. Journal of Pharmaceutical Negative Results, 3713-3717.

Dejene, B. E., Abuhay, T. M., & Bogale, D. S. (2022). Predicting the level of anemia among Ethiopian pregnant women using homogeneous ensemble machine learning algorithm. BMC Medical Informatics and Decision Making, 22(1), 1-11. DOI: https://doi.org/10.1186/s12911-022-01992-6

Gupta, J. P., Singh, A., & Kumar, R. K. (2021). A computer-based disease prediction and medicine recommendation system using machine learning approach. Int J Adv Res Eng Technol (IJARET), 12(3), 673-683.

Abd Rahman, R., Idris, I. B., Md Isa, Z., & Abd Rahman, R. (2022). The effectiveness of a theory-based intervention program for pregnant women with anemia: A randomized control trial. Plos one, 17(12), e0278192. DOI: https://doi.org/10.1371/journal.pone.0278192

Zahirzada, A., Zaheer, N., & Shahpoor, M. A. (2023). Machine Learning Algorithms to Predict Anemia in Children Under the Age of Five Years in Afghanistan: A Case of Kunduz Province. Journal of Survey in Fisheries Sciences, 10(4S), 752-762.

Asare, Justice & Appiahene, Peter & Donkoh, Emmanuel & Dimauro, Giovanni. (2023). Iron Deficiency Anemia Detection using Machine Learning Models: A Comparative Study of Fingernails, Palm and Conjunctiva of the Eye Images. 10.22541/au.167570558.82410707/v1. DOI: https://doi.org/10.22541/au.167570558.82410707/v1

Nancy Noella, R. S., & Priyadarshini, J. (2023). Machine learning algorithms for the diagnosis of Alzheimer and Parkinson disease. Journal of Medical Engineering & Technology, 47(1), 35-43. DOI: https://doi.org/10.1080/03091902.2022.2097326

Dimauro, G., Griseta, M. E., Camporeale, M. G., Clemente, F., Guarini, A., & Maglietta, R. (2023). An intelligent non-invasive system for automated diagnosis of anemia exploiting a novel dataset. Artificial Intelligence in Medicine, 136, 102477. DOI: https://doi.org/10.1016/j.artmed.2022.102477

Sarkodie, K., Fergusson-Rees, A., Abdulkadir, M., & Asiedu, N. Y. (2023). Gas-liquid flow regime identification via a non-intrusive optical sensor combined with polynomial regression and linear discriminant analysis. Annals of Nuclear Energy, 180, 109424. DOI: https://doi.org/10.1016/j.anucene.2022.109424

Sharma, S., & Jain, A. (2023). Hybrid ensemble learning with feature selection for sentiment classification in social media. In Research Anthology on Applying Social Networking Strategies to Classrooms and Libraries (pp. 1183-1203). IGI Global. DOI: https://doi.org/10.4018/978-1-6684-7123-4.ch064

Asare, J. W., Appiahene, P., & Donkoh, E. T. (2023). Detection of anaemia using medical images: A comparative study of machine learning algorithms–A systematic literature review. Informatics in Medicine Unlocked, 101283. DOI: https://doi.org/10.1016/j.imu.2023.101283

Zhang, A., Lou, J., Pan, Z., Luo, J., Zhang, X., Zhang, H., ... & Chen, L. (2022). Prediction of anemia using facial images and deep learning technology in the emergency department. Frontiers in Public Health, 10, 3917. DOI: https://doi.org/10.3389/fpubh.2022.964385

Kilicarslan, S., Celik, M., & Sahin, Ş. (2021). Hybrid models based on genetic algorithm and deep learning algorithms for nutritional Anemia disease classification. Biomedical Signal Processing and Control, 63, 102231. DOI: https://doi.org/10.1016/j.bspc.2020.102231

Yeruva, S., Varalakshmi, M. S., Gowtham, B. P., Chandana, Y. H., & Prasad, P. K. (2021). Identification of sickle cell anemia using deep neural networks. Emerging Science Journal, 5(2), 200-210. DOI: https://doi.org/10.28991/esj-2021-01270

Balaji, E., Brindha, D., Elumalai, V. K., & Vikrama, R. (2021). Automatic and non-invasive Parkinson’s disease diagnosis and severity rating using LSTM network. Applied Soft Computing, 108, 107463. DOI: https://doi.org/10.1016/j.asoc.2021.107463

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Published

20-10-2023

How to Cite

1.
Shweta N, Pande SD. Prediction of Anemia using various Ensemble Learning and Boosting Techniques. EAI Endorsed Trans Perv Health Tech [Internet]. 2023 Oct. 20 [cited 2024 May 26];9. Available from: https://publications.eai.eu/index.php/phat/article/view/4197