AIRCC's International Journal of Computer Science and Information Technology

Open Access Open Access  Restricted Access Subscription Access

Prediction of Anemia Using Machine Learning Algorithms


Affiliations
1 Department of Computer Science and Engineering, Kathmandu University, Dhulikhel, Nepal
 

Anemia is a state of poor health where there is presence of low amount of red blood cell in blood stream. This research aims to design a model for prediction of Anemia in children under 5 years of age using Complete Blood Count reports. Data are collected from Kanti Children Hospital which consist of 700 data records. Then they are preprocessed, normalized, balanced and selected machine learning algorithms were applied. It is followed by verification, validation along with result analysis. Random Forest is the best performer which showed accuracy of 98.4%. Finally, Feature Selection as well as Ensemble Learning methods, Voting, Stacking, Bagging and Boosting were applied to improve the performance of algorithms. Selecting the best performer algorithm, stacking with other algorithms, bagging it, boosting it are very much crucial to improve accuracy despite of any time issue for prediction of anemia in children below 5 years of age.

Keywords

Machine Learning, Anemia, Children, Prediction, Algorithm, Accuracy.
User
Notifications
Font Size

  • L. Wang, M. Li, S. E. Dill, Y. Hu, and S. Rozelle, "Dynamic Anemia Status from Infancy to Preschool-Age: Evidence from Rural China," International journal of environmental research and public health, vol. 16, no. 15, pp. 2761, 2019.

  • V. Arun, V. Shyam, and S. K. Padma, "Privacy of health information in telemedicine on private cloud," Int J Family Med Med Sci Res, vol. 4, no. 189, pp. 2, 2015.

  • N. Soundarya and P. Suganthi, "A review on anaemia–types, causes, symptoms and their treatments," Journal of science and technology investigation, vol. 1, no. 1, 2017.

  • N. Alli, J. Vaughan, and M. Patel, "Anaemia: Approach to diagnosis," SAMJ: South African Medical Journal, vol. 107, no. 1, pp. 23-27, 2017.

  • H. Bhavsar and A. Ganatra, "A comparative study of training algorithms for supervised machine learning," International Journal of Soft Computing and Engineering (IJSCE), vol. 2, no. 4, pp. 2231-2307, 2012.

  • S. Uddin, A. Khan, M. E. Hossain, and M. A. Moni, "Comparing different supervised machine learning algorithms for disease prediction," BMC Medical Informatics and Decision Making, vol. 19, no. 1, pp. 1-16, 2019.

  • M. Fatima and M. Pasha, "Survey of machine learning algorithms for disease diagnostic," Journal of Intelligent Learning Systems and Applications, vol. 9, no. 01, pp. 1, 2017.

  • M. Jaiswal, A. Srivastava, and T. J. Siddiqui, "Machine Learning Algorithms for Anemia Disease Prediction," in Recent Trends in Communication, Computing, and Electronics, Springer, Singapore, 2019, pp. 463-469.

  • P. T. Dalvi and N. Vernekar, "Anemia detection using ensemble learning techniques and statistical models," in 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), 2016, pp. 1747-1751.

  • M. Abdullah and S. Al-Asmari, "Anemia types prediction based on data mining classification algorithms," Communication, Management and Information Technology–Sampaio de Alencar (Ed.).

  • J. R. Khan, S. Chowdhury, H. Islam, and E. Raheem, "Machine learning algorithms to predict the childhood anemia in Bangladesh," Journal of Data Science, vol. 17, no. 1, pp. 195-218, 2019.

  • P. Anand, R. Gupta, and A. Sharma, "Prediction of Anemia among children using Machine Learning Algorithms," International Journal of Electronics Engineering, vol. 11, no. 2, pp. 469-480, 2019.

  • J. Zhang and W. Tang, "Building a prediction model for iron deficiency anemia among infants in Shanghai, China," Food Science & Nutrition, 2019.

  • J. E. Ewusie, C. Ahiadeke, J. Beyene, and J. S. Hamid, "Prevalence of anemia among under-5 children in the Ghanaian population: estimates from the Ghana demographic and health survey," BMC public health, vol. 14, no. 1, p. 626, 2014.


Abstract Views: 216

PDF Views: 112




  • Prediction of Anemia Using Machine Learning Algorithms

Abstract Views: 216  |  PDF Views: 112

Authors

Prakriti Dhakal
Department of Computer Science and Engineering, Kathmandu University, Dhulikhel, Nepal
Santosh Khanal
Department of Computer Science and Engineering, Kathmandu University, Dhulikhel, Nepal
Rabindra Bista
Department of Computer Science and Engineering, Kathmandu University, Dhulikhel, Nepal

Abstract


Anemia is a state of poor health where there is presence of low amount of red blood cell in blood stream. This research aims to design a model for prediction of Anemia in children under 5 years of age using Complete Blood Count reports. Data are collected from Kanti Children Hospital which consist of 700 data records. Then they are preprocessed, normalized, balanced and selected machine learning algorithms were applied. It is followed by verification, validation along with result analysis. Random Forest is the best performer which showed accuracy of 98.4%. Finally, Feature Selection as well as Ensemble Learning methods, Voting, Stacking, Bagging and Boosting were applied to improve the performance of algorithms. Selecting the best performer algorithm, stacking with other algorithms, bagging it, boosting it are very much crucial to improve accuracy despite of any time issue for prediction of anemia in children below 5 years of age.

Keywords


Machine Learning, Anemia, Children, Prediction, Algorithm, Accuracy.

References