Prediction of hydroxyurea effect on sickle cell anemia patients using machine learning method

B.K. Singh; A. Ojha; K.K. Bhoi; A. Bissoyi; P.K. Patra

doi:10.1007/978-981-15-6329-4_37

Prediction of hydroxyurea effect on sickle cell anemia patients using machine learning method

B.K. Singh, A. Ojha, K.K. Bhoi, A. Bissoyi, P.K. Patra

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

Abstract

The objective of this paper is to predict the effect of hydroxyurea of individuals suffering from Sickle Cell Disease (SCD) using predictive machine learning techniques. Nowadays worldwide sickle cell disease becomes the most common inherited disease, which remains associated with long life morbidity and makes life expectancy low or we can say it reduces the lifespan of the concerned person. To overcome such diseases, a chemotherapeutic drug Hydroxyurea is taken by the concerned patient to re-mediate clinical problems of sickle cell disease. This paper discusses the use of a machine learning approach to predict the effects of the drug Hydroxyurea (HU) treatment in different sickle cell anemic patients. Fetal hemoglobin before and after treatment of HU establishes the basis for classification. Support vector machine has been used for classification and the dataset is rigorously analyzed for different data division methods and we got the maximum accuracy level of 94%.

Original language	English
Title of host publication	Advances in Biomedical Engineering and Technology
Subtitle of host publication	Select Proceedings of ICBEST 2018
Editors	Albert A. Rizvanov , Bikesh Kumar Singh , Padma Ganasala
Publisher	Springer Nature
Pages	447–457
Number of pages	11
ISBN (Electronic)	9789811563294
ISBN (Print)	9789811563287
DOIs	https://doi.org/10.1007/978-981-15-6329-4_37
Publication status	Published - 29 Sept 2020

Publication series

Name	Lecture Notes in Bioengineering

Name	ICBEST: International Conference on Biomedical Engineering Science and Technology

Keywords

Sickle cell anemia
Hydroxyurea
Support vector machine

Access to Document

10.1007/978-981-15-6329-4_37

Cite this

Singh, B. K., Ojha, A., Bhoi, K. K., Bissoyi, A., & Patra, P. K. (2020). Prediction of hydroxyurea effect on sickle cell anemia patients using machine learning method. In A. A. R., B. K. S., & P. G. (Eds.), Advances in Biomedical Engineering and Technology: Select Proceedings of ICBEST 2018 (pp. 447–457). (Lecture Notes in Bioengineering), (ICBEST: International Conference on Biomedical Engineering Science and Technology). Springer Nature. https://doi.org/10.1007/978-981-15-6329-4_37

Singh, B.K. ; Ojha, A. ; Bhoi, K.K. et al. / Prediction of hydroxyurea effect on sickle cell anemia patients using machine learning method. Advances in Biomedical Engineering and Technology: Select Proceedings of ICBEST 2018. editor / Albert A. Rizvanov ; Bikesh Kumar Singh ; Padma Ganasala. Springer Nature, 2020. pp. 447–457 (Lecture Notes in Bioengineering). (ICBEST: International Conference on Biomedical Engineering Science and Technology).

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title = "Prediction of hydroxyurea effect on sickle cell anemia patients using machine learning method",

abstract = "The objective of this paper is to predict the effect of hydroxyurea of individuals suffering from Sickle Cell Disease (SCD) using predictive machine learning techniques. Nowadays worldwide sickle cell disease becomes the most common inherited disease, which remains associated with long life morbidity and makes life expectancy low or we can say it reduces the lifespan of the concerned person. To overcome such diseases, a chemotherapeutic drug Hydroxyurea is taken by the concerned patient to re-mediate clinical problems of sickle cell disease. This paper discusses the use of a machine learning approach to predict the effects of the drug Hydroxyurea (HU) treatment in different sickle cell anemic patients. Fetal hemoglobin before and after treatment of HU establishes the basis for classification. Support vector machine has been used for classification and the dataset is rigorously analyzed for different data division methods and we got the maximum accuracy level of 94\%.",

keywords = "Sickle cell anemia, Hydroxyurea, Support vector machine",

author = "B.K. Singh and A. Ojha and K.K. Bhoi and A. Bissoyi and P.K. Patra",

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Singh, BK, Ojha, A, Bhoi, KK, Bissoyi, A & Patra, PK 2020, Prediction of hydroxyurea effect on sickle cell anemia patients using machine learning method. in AAR, BKS & PG (eds), Advances in Biomedical Engineering and Technology: Select Proceedings of ICBEST 2018. Lecture Notes in Bioengineering, ICBEST: International Conference on Biomedical Engineering Science and Technology, Springer Nature, pp. 447–457. https://doi.org/10.1007/978-981-15-6329-4_37

Prediction of hydroxyurea effect on sickle cell anemia patients using machine learning method. / Singh, B.K.; Ojha, A.; Bhoi, K.K. et al.
Advances in Biomedical Engineering and Technology: Select Proceedings of ICBEST 2018. ed. / Albert A. Rizvanov; Bikesh Kumar Singh; Padma Ganasala. Springer Nature, 2020. p. 447–457 (Lecture Notes in Bioengineering), (ICBEST: International Conference on Biomedical Engineering Science and Technology).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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AB - The objective of this paper is to predict the effect of hydroxyurea of individuals suffering from Sickle Cell Disease (SCD) using predictive machine learning techniques. Nowadays worldwide sickle cell disease becomes the most common inherited disease, which remains associated with long life morbidity and makes life expectancy low or we can say it reduces the lifespan of the concerned person. To overcome such diseases, a chemotherapeutic drug Hydroxyurea is taken by the concerned patient to re-mediate clinical problems of sickle cell disease. This paper discusses the use of a machine learning approach to predict the effects of the drug Hydroxyurea (HU) treatment in different sickle cell anemic patients. Fetal hemoglobin before and after treatment of HU establishes the basis for classification. Support vector machine has been used for classification and the dataset is rigorously analyzed for different data division methods and we got the maximum accuracy level of 94%.

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Singh BK, Ojha A, Bhoi KK, Bissoyi A, Patra PK. Prediction of hydroxyurea effect on sickle cell anemia patients using machine learning method. In AAR, BKS, PG, editors, Advances in Biomedical Engineering and Technology: Select Proceedings of ICBEST 2018. Springer Nature. 2020. p. 447–457. (Lecture Notes in Bioengineering). (ICBEST: International Conference on Biomedical Engineering Science and Technology). doi: 10.1007/978-981-15-6329-4_37

Prediction of hydroxyurea effect on sickle cell anemia patients using machine learning method

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