Using Data Mining Algorithms for Thalassemia Risk Prediction

Ngozi Chidozie Egejuru; Sekoni Olayinka Olusanya; Adanze Onyenonachi Asinobi; Omotayo Joseph Adeyemi; Victor Oluwatimilehin Adebayo; Peter Adebayo Idowu

doi:doi:10.11648/j.ijbse.20190702.12

Using Data Mining Algorithms for Thalassemia Risk Prediction

Ngozi Chidozie Egejuru, Sekoni Olayinka Olusanya, Adanze Onyenonachi Asinobi, Omotayo Joseph Adeyemi, Victor Oluwatimilehin Adebayo, Peter Adebayo Idowu

Published in International Journal of Biomedical Science and Engineering (Volume 7, Issue 2)

Received: 7 August 2019 Accepted: 23 August 2019 Published: 6 September 2019

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Abstract

This study predict the risk of thalassemia in all age groups based on identified risk of thalassemia. Knowledge about the risk factors for thalassemia was identified using structural interview with experienced medical personnel and questionnaire which was used to collect empirical medical database on the parameters. Supervised machine learning algorithms was used to formulate the predictive model for risk of thalassemia using the parameters and data identified and collected. The predictive model for the risk of thalassemia was simulated using the Waikato Environment for Knowledge Analysis (WEKA). The simulated model was validated using the historical data collected from the hospitals explaining the parameters and the risk of Thalassemia. The results of the study showed that following the collection of data from 51 patients, the parameters identified included demographic variables like gender, age, marital status, ethnicity and social class while the clinical variables included family history, spleen enlargement, diabetes, urine colour changes and parent carriers while the distribution of the risk was 43% no cases, 10% low cases, 16% moderate cases and 31% high cases. The study concluded that using the multi-layer perceptron for the prediction of Thalassemia will improve the decision making process within the healthcare service concerning Thalassemia.

Published in	International Journal of Biomedical Science and Engineering (Volume 7, Issue 2)
DOI	10.11648/j.ijbse.20190702.12
Page(s)	33-44
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Thallasemia, Anaemia, Predictive Model, Naïve Bayes, Classifier, Multilayer Perceptron

References

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[6]	Siteti, M. C., Namasaka, S. D., Ariya, O. P., Injete, S. D. and Wanyonyi, W. A. (2014). Anaemia in Pregnancy: Prevalence and Possible Risk Factors in Kakamega County, Kenya. Science Journal of Public Health 2 (3): 216-222.
[7]	World Health Organization (WHO) (1992). The prevalence of Anaemia in women; a tabulation of available information. Geneva: World Health Organization.
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[9]	Kishore, C. R., Rao, K. P. and Murthy, G. (2015). Performance Evaluation of Entropy and Gini Using Threaded and Non-Threaded ID3 on Anaemia Dataset. Life 6 (10): 10-12.
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[12]	Huang, C.-L., Liao, H.-C. and Chen, M.-C. (2008). Prediction Model Building and Feature Selection with Support Vector Machines in Cancer Diagnosis. Journal of Expert Systems with Applications 34 (1): 578-587.
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[14]	Amin, N. and Habib, A. (2015). Conparison of Different Classification Technoiques Using WEKA for Hematological Data. American Journal of Engineering Research (AJER) 4 (3): 55-61.
[15]	Idowu, P. A., Aladekomo, T. A., Williams, K. O. and Balogun, J. A. (2015). Predictive Model for Likelihood of Survival of Sickle Cell Anemia (SCA) among Pediatric Patients using Fuzzy Logic. Transactions in Networks and Communications 31 (1): 31-44.

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Ngozi Chidozie Egejuru, Sekoni Olayinka Olusanya, Adanze Onyenonachi Asinobi, Omotayo Joseph Adeyemi, Victor Oluwatimilehin Adebayo, et al. (2019). Using Data Mining Algorithms for Thalassemia Risk Prediction. International Journal of Biomedical Science and Engineering, 7(2), 33-44. https://doi.org/10.11648/j.ijbse.20190702.12

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ACS Style

Ngozi Chidozie Egejuru; Sekoni Olayinka Olusanya; Adanze Onyenonachi Asinobi; Omotayo Joseph Adeyemi; Victor Oluwatimilehin Adebayo, et al. Using Data Mining Algorithms for Thalassemia Risk Prediction. Int. J. Biomed. Sci. Eng. 2019, 7(2), 33-44. doi: 10.11648/j.ijbse.20190702.12

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AMA Style

Ngozi Chidozie Egejuru, Sekoni Olayinka Olusanya, Adanze Onyenonachi Asinobi, Omotayo Joseph Adeyemi, Victor Oluwatimilehin Adebayo, et al. Using Data Mining Algorithms for Thalassemia Risk Prediction. Int J Biomed Sci Eng. 2019;7(2):33-44. doi: 10.11648/j.ijbse.20190702.12

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@article{10.11648/j.ijbse.20190702.12,
  author = {Ngozi Chidozie Egejuru and Sekoni Olayinka Olusanya and Adanze Onyenonachi Asinobi and Omotayo Joseph Adeyemi and Victor Oluwatimilehin Adebayo and Peter Adebayo Idowu},
  title = {Using Data Mining Algorithms for Thalassemia Risk Prediction},
  journal = {International Journal of Biomedical Science and Engineering},
  volume = {7},
  number = {2},
  pages = {33-44},
  doi = {10.11648/j.ijbse.20190702.12},
  url = {https://doi.org/10.11648/j.ijbse.20190702.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijbse.20190702.12},
  abstract = {This study predict the risk of thalassemia in all age groups based on identified risk of thalassemia. Knowledge about the risk factors for thalassemia was identified using structural interview with experienced medical personnel and questionnaire which was used to collect empirical medical database on the parameters. Supervised machine learning algorithms was used to formulate the predictive model for risk of thalassemia using the parameters and data identified and collected. The predictive model for the risk of thalassemia was simulated using the Waikato Environment for Knowledge Analysis (WEKA). The simulated model was validated using the historical data collected from the hospitals explaining the parameters and the risk of Thalassemia. The results of the study showed that following the collection of data from 51 patients, the parameters identified included demographic variables like gender, age, marital status, ethnicity and social class while the clinical variables included family history, spleen enlargement, diabetes, urine colour changes and parent carriers while the distribution of the risk was 43% no cases, 10% low cases, 16% moderate cases and 31% high cases. The study concluded that using the multi-layer perceptron for the prediction of Thalassemia will improve the decision making process within the healthcare service concerning Thalassemia.},
 year = {2019}
}

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TY  - JOUR
T1  - Using Data Mining Algorithms for Thalassemia Risk Prediction
AU  - Ngozi Chidozie Egejuru
AU  - Sekoni Olayinka Olusanya
AU  - Adanze Onyenonachi Asinobi
AU  - Omotayo Joseph Adeyemi
AU  - Victor Oluwatimilehin Adebayo
AU  - Peter Adebayo Idowu
Y1  - 2019/09/06
PY  - 2019
N1  - https://doi.org/10.11648/j.ijbse.20190702.12
DO  - 10.11648/j.ijbse.20190702.12
T2  - International Journal of Biomedical Science and Engineering
JF  - International Journal of Biomedical Science and Engineering
JO  - International Journal of Biomedical Science and Engineering
SP  - 33
EP  - 44
PB  - Science Publishing Group
SN  - 2376-7235
UR  - https://doi.org/10.11648/j.ijbse.20190702.12
AB  - This study predict the risk of thalassemia in all age groups based on identified risk of thalassemia. Knowledge about the risk factors for thalassemia was identified using structural interview with experienced medical personnel and questionnaire which was used to collect empirical medical database on the parameters. Supervised machine learning algorithms was used to formulate the predictive model for risk of thalassemia using the parameters and data identified and collected. The predictive model for the risk of thalassemia was simulated using the Waikato Environment for Knowledge Analysis (WEKA). The simulated model was validated using the historical data collected from the hospitals explaining the parameters and the risk of Thalassemia. The results of the study showed that following the collection of data from 51 patients, the parameters identified included demographic variables like gender, age, marital status, ethnicity and social class while the clinical variables included family history, spleen enlargement, diabetes, urine colour changes and parent carriers while the distribution of the risk was 43% no cases, 10% low cases, 16% moderate cases and 31% high cases. The study concluded that using the multi-layer perceptron for the prediction of Thalassemia will improve the decision making process within the healthcare service concerning Thalassemia.
VL  - 7
IS  - 2
ER  -

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Author Information

Ngozi Chidozie Egejuru

Department of Computer Science, Hallmark University, Ijebu-Itele, Nigeria
Sekoni Olayinka Olusanya

Department of Computer Science, Tai Solarin University of Education, Ijebu Ode, Nigeria
Adanze Onyenonachi Asinobi

Department of Pediatrics, College of Medicine, University of Ibadan, Ibadan, Nigeria
Omotayo Joseph Adeyemi

Department of Computer Science, Tai Solarin University of Education, Ijebu Ode, Nigeria
Victor Oluwatimilehin Adebayo

Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria
Peter Adebayo Idowu

Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria

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APA Style

Ngozi Chidozie Egejuru, Sekoni Olayinka Olusanya, Adanze Onyenonachi Asinobi, Omotayo Joseph Adeyemi, Victor Oluwatimilehin Adebayo, et al. (2019). Using Data Mining Algorithms for Thalassemia Risk Prediction. International Journal of Biomedical Science and Engineering, 7(2), 33-44. https://doi.org/10.11648/j.ijbse.20190702.12

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ACS Style

Ngozi Chidozie Egejuru; Sekoni Olayinka Olusanya; Adanze Onyenonachi Asinobi; Omotayo Joseph Adeyemi; Victor Oluwatimilehin Adebayo, et al. Using Data Mining Algorithms for Thalassemia Risk Prediction. Int. J. Biomed. Sci. Eng. 2019, 7(2), 33-44. doi: 10.11648/j.ijbse.20190702.12

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AMA Style

Ngozi Chidozie Egejuru, Sekoni Olayinka Olusanya, Adanze Onyenonachi Asinobi, Omotayo Joseph Adeyemi, Victor Oluwatimilehin Adebayo, et al. Using Data Mining Algorithms for Thalassemia Risk Prediction. Int J Biomed Sci Eng. 2019;7(2):33-44. doi: 10.11648/j.ijbse.20190702.12

Copy | Download

@article{10.11648/j.ijbse.20190702.12,
  author = {Ngozi Chidozie Egejuru and Sekoni Olayinka Olusanya and Adanze Onyenonachi Asinobi and Omotayo Joseph Adeyemi and Victor Oluwatimilehin Adebayo and Peter Adebayo Idowu},
  title = {Using Data Mining Algorithms for Thalassemia Risk Prediction},
  journal = {International Journal of Biomedical Science and Engineering},
  volume = {7},
  number = {2},
  pages = {33-44},
  doi = {10.11648/j.ijbse.20190702.12},
  url = {https://doi.org/10.11648/j.ijbse.20190702.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijbse.20190702.12},
  abstract = {This study predict the risk of thalassemia in all age groups based on identified risk of thalassemia. Knowledge about the risk factors for thalassemia was identified using structural interview with experienced medical personnel and questionnaire which was used to collect empirical medical database on the parameters. Supervised machine learning algorithms was used to formulate the predictive model for risk of thalassemia using the parameters and data identified and collected. The predictive model for the risk of thalassemia was simulated using the Waikato Environment for Knowledge Analysis (WEKA). The simulated model was validated using the historical data collected from the hospitals explaining the parameters and the risk of Thalassemia. The results of the study showed that following the collection of data from 51 patients, the parameters identified included demographic variables like gender, age, marital status, ethnicity and social class while the clinical variables included family history, spleen enlargement, diabetes, urine colour changes and parent carriers while the distribution of the risk was 43% no cases, 10% low cases, 16% moderate cases and 31% high cases. The study concluded that using the multi-layer perceptron for the prediction of Thalassemia will improve the decision making process within the healthcare service concerning Thalassemia.},
 year = {2019}
}

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TY  - JOUR
T1  - Using Data Mining Algorithms for Thalassemia Risk Prediction
AU  - Ngozi Chidozie Egejuru
AU  - Sekoni Olayinka Olusanya
AU  - Adanze Onyenonachi Asinobi
AU  - Omotayo Joseph Adeyemi
AU  - Victor Oluwatimilehin Adebayo
AU  - Peter Adebayo Idowu
Y1  - 2019/09/06
PY  - 2019
N1  - https://doi.org/10.11648/j.ijbse.20190702.12
DO  - 10.11648/j.ijbse.20190702.12
T2  - International Journal of Biomedical Science and Engineering
JF  - International Journal of Biomedical Science and Engineering
JO  - International Journal of Biomedical Science and Engineering
SP  - 33
EP  - 44
PB  - Science Publishing Group
SN  - 2376-7235
UR  - https://doi.org/10.11648/j.ijbse.20190702.12
AB  - This study predict the risk of thalassemia in all age groups based on identified risk of thalassemia. Knowledge about the risk factors for thalassemia was identified using structural interview with experienced medical personnel and questionnaire which was used to collect empirical medical database on the parameters. Supervised machine learning algorithms was used to formulate the predictive model for risk of thalassemia using the parameters and data identified and collected. The predictive model for the risk of thalassemia was simulated using the Waikato Environment for Knowledge Analysis (WEKA). The simulated model was validated using the historical data collected from the hospitals explaining the parameters and the risk of Thalassemia. The results of the study showed that following the collection of data from 51 patients, the parameters identified included demographic variables like gender, age, marital status, ethnicity and social class while the clinical variables included family history, spleen enlargement, diabetes, urine colour changes and parent carriers while the distribution of the risk was 43% no cases, 10% low cases, 16% moderate cases and 31% high cases. The study concluded that using the multi-layer perceptron for the prediction of Thalassemia will improve the decision making process within the healthcare service concerning Thalassemia.
VL  - 7
IS  - 2
ER  -

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