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Using Data Mining Algorithms for Thalassemia Risk Prediction

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
[1] Gretchen Holm and Kristeen Cherney (2017). Thalassemia. Available from https://www.healthline.com/health/thalassemia [Access on 7th August, 2018].
[2] World Health Organization (WHO) (1968). Nutritional anemia: report of a WHO Scientific Group. Geneva, Switzerland: World Health Organization.
[3] World Health Organization (2011). WHO Vitamin and Mineral Nutrition. Geneva, Switzerland: World Health Organization.
[4] Ivoke, N., Eyo, J. E., Ivoke, O. N., Nwani, C. D., Odii, E. C., Asogwa, C. N., Ekeh, F. N. and Atama, C. I. (2013). Anaemia Prevalence and Associated Factors among Women Attending Antenatal Clinics in South-Western Ebonyi State, Nigeria. International Journal of Medicine and Medical Sciences 46 (4): 1354-1359.
[5] Osungbade, K. O. and Oladunjoye, A. O. (2012). Preventive treatments of iron deficiency Anaemia in pregnancy: a review of their effectiveness and implications for health system strengthening. Journal of Pregnancy 2012: 1-7.
[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.
[8] Kaur, P., Singh, M. and Josan, G. (2015). Classification and Prediction Based Data Mining Algorithms to Predict Slow Learners in Education Sector. Procedia Computer Science 57: 500-508.
[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.
[10] Chuang L-Y, Wu K-C, Chang H-W, Yang C-H (2011) Support vector machine-based prediction for oral cancer using four snps in DNA repair genes. In: Proceedings of the international multiconference of engineers and computer scientists, March 16–18 2011.
[11] Hu, Z. Fan, C., Oh, D. S., Marron, J. S., He, X., Qaqish, B. F., Livasy, C. and Carey, L. A. (2006). The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics 7: 96-107.
[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.
[13] Curiac, D. I., Vasile, G., Banias, O., Volosencu, C and Albu, A. (2009). Bayesian Network Model for Diagnosis of Psychiatric Diseases. In Proceedings of the ITI 2009 31st International Conference on Information Technology Interfaces held on June 22-25, 2009 at Cavtat, Croatia: 61-66.
[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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  • 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

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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
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    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
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    ER  - 

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Author Information
  • Department of Computer Science, Hallmark University, Ijebu-Itele, Nigeria

  • Department of Computer Science, Tai Solarin University of Education, Ijebu Ode, Nigeria

  • Department of Pediatrics, College of Medicine, University of Ibadan, Ibadan, Nigeria

  • Department of Computer Science, Tai Solarin University of Education, Ijebu Ode, Nigeria

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

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

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