American Journal of Theoretical and Applied Statistics

Research Article | | Peer-Reviewed |

Classification of Contraceptive Use Among Undergraduate Students Using a Supervised Machine Learning Technique

Received: Sep. 26, 2023    Accepted: Oct. 12, 2023    Published: Oct. 28, 2023
Views:       Downloads:

Share

Abstract

The Kenyan government in partnership with other stakeholders involved in providing family planning services have put in place various strategies and policies to increase uptake of contraceptives. This results in an increase in contraceptive prevalence rate (CPR), reduction of both total fertility rate (TFR) and sexually transmitted infections (STIs). Despite the various strategies and policies, the total fertility rate still remains high, while CPR has been unattained, respectively. The aim of this study was to classify contraceptives use among undergraduate students using a supervised machine learning technique. The target population constituted students at Jomo Kenyatta University of Agriculture and Technology (JKUAT) (Eldoret Campus). The study applied simple random sampling technique to obtain data from a sample of 252 using structured questionnaires. A decision tree classifier based on CHAID and C5.0 algorithms were used for classification. Pearson Chi-Squared statistic was used as feature selection technique to rank significant factors influencing contraceptives use based on their Chi scores. The findings show that the use of Chi-Squared feature selection led to contraceptives factors that were ranked higher having higher classification performance. The fitted decision tree model based on CHAID algorithm had a higher classification accuracy of 64.68% with 195 correct classifications as compared to the C5.0 decision tree model with accuracy of 61.18% with 163 correct classifications. The study findings contribute to a better insight on the classifications of contraceptives use among undergraduate students in Kenya. Hence, the government of Kenya can implement policies to enhance contraceptives awareness.

DOI 10.11648/j.ajtas.20231205.14
Published in American Journal of Theoretical and Applied Statistics ( Volume 12, Issue 5, September 2023 )
Page(s) 120-128
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), 2024. Published by Science Publishing Group

Keywords

Chi-Squared Feature Selection, Decision Tree Classifier, CHAID Algorithm, C5.0 Algorithm, Contraceptive’s Use

References
[1] Ahmed, S., Li, Q., Liu, L., & Tsui, A. O. (2012). Maternal deaths averted by contraceptive use: an analysis of 172 countries. The Lancet, 380 (9837), 111-125.
[2] Brunner Huber, L. R., Smith, K., Sha, W., & Vick, T. (2018). Interbirth interval and pregnancy complications and outcomes: findings from the pregnancy risk assessment monitoring system. Journal of Midwifery & Women's Health, 63 (4), 436-445.
[3] Chiang, H. J., Tseng, C. C., & Torng, C. C. (2013). A retrospective analysis of prognostic indicators in dental implant therapy using the C5. 0 decision tree algorithm. Journal of Dental Sciences, 8 (3), 248-255.
[4] Collis, J. & Hussey, R. (2013) Business Resarch. England: Palgrave Macmillan.
[5] Darroch, J. E., & Singh, S. (2011). Estimating unintended pregnancies averted from couple-years of protection (CYP). New York: Guttmacher Institute, 1 (10).
[6] Darroch, J. E., Woog, V., Bankole, A., & Ashford, L. S. (2016). Adding it up: costs and benefits of meeting the contraceptive needs of adolescents.
[7] Dauda, K. A., Babatunde, A. N., Olorede, K. O., Abdulsalam, S. O., & Ogundokun, O. R. (2018). Effectiveness of Contraceptive Usage among Reproductive Ages in Nigeria Using Artificial Neural Network (ANN). Computing & Information Systems, 22 (1).
[8] Ftye, M., Letta, A., & Achamyeleh, B. (2022). designing a predictive model for the likelihood of contraceptive method usage in ethiopia. Cosmos Journal of Engineering & Technology, 12 (1).
[9] Haq, I., Hossain, M. I., Rahman, M. M., Methun, M. I. H., Talukder, A., Habib, M. J., & Hossain, M. S. (2022). Machine Learning Algorithm-Based Contraceptive Practice among Ever-Married Women in Bangladesh: A Hierarchical Machine Learning Classification Approach. In Machine Learning and Data Mining-Annual Volume 2022. IntechOpen.
[10] Ibad, M., Lutfiya, I., Handayani, D., Fasya, A. H. Z., Herowati, D., & Sari, M. P. (2023, May). Classification analysis using decision tree on factors that influence the selection of contraception equipment in East Java Province. In AIP Conference Proceedings (Vol. 2595, No. 1). AIP Publishing.
[11] Jiang, W., & Ha, L. (2020). Smartphones or computers for online sex education? A contraception information seeking model for Chinese college students. Sex Education, 20 (4), 457-476.
[12] Mustaqim, B. W., & Surarso, B. (2020). combination of synthetic minority oversampling technique (smote) and backpropagation neural network to contraceptive iud prediction.
[13] Pandya, R., & Pandya, J. (2015). C5. 0 algorithm to improved decision tree with feature selection and reduced error pruning. International Journal of Computer Applications, 117 (16), 18-21.
[14] Podolskyi, V., Gemzell-Danielsson, K., & Marions, L. (2018). Contraceptive experience and perception, a survey among Ukrainian women. BMC Women's Health, 18, 1-6.
[15] Rastogi, R., & Shim, K. (2000). PUBLIC: A decision tree classifier that integrates building and pruning. Data Mining and Knowledge Discovery, 4, 315-344.
[16] Saunders, M. N., Thornhill, A. & Lewis, P. (2019). Research Methods for Business Students (Eighth Edition ed.). London, UK: Pearson.
[17] Sun, X., Liu, X., Shi, Y., Wang, Y., Wang, P., & Chang, C. (2013). Determinants of risky sexual behavior and condom use among college students in China. AIDS care, 25 (6), 775-783.
[18] Tangirala, S. (2020). Evaluating the impact of GINI index and information gain on classification using decision tree classifier algorithm. International Journal of Advanced Computer Science and Applications, 11 (2), 612-619.
[19] UNFP (2010). United Nations Population Fund. Sexual and Reproductive Health for all: Reducing Poverty, Advancing Development and Protecting Human Rights. New York, New York, United States.
[20] Vieira, S. M., Kaymak, U., & Sousa, J. M. (2010, July). Cohen's kappa coefficient as a performance measure for feature selection. In International conference on fuzzy systems (pp. 1-8). IEEE.
[21] Wang, H., Long, L., Cai, H., Wu, Y., Xu, J., Shu, C.,... & Yin, P. (2015). Contraception and unintended pregnancy among unmarried female university students: a cross-sectional study from China. PloS one, 10 (6), e0130212.
[22] Wang, Y., Chen, M., Tan, S., Qu, X., Wang, H., Liang, X. & Tang, K. (2020). The socioeconomic and lifestyle determinants of contraceptive use among Chinese college students: a cross-sectional study. Reproductive Health, 17, 1-11.
[23] Zhou, H., Wang, X. Y., Ye, F., Gu, H. H., Zeng, X. P. L., & Wang, Y. (2012). Contraceptive knowledge, attitudes and behavior about sexuality among college students in Beijing, China. Chinese medical journal, 125 (06), 1153-1157.
[24] Lee, S., Lee, C., Mun, K. G., & Kim, D. (2022). Decision Tree Algorithm Considering Distances Between Classes. IEEE Access, 10, 69750-69756.
[25] Jin, L., & Myers, S. C. (2006). R2 around the World: NNew Theory and New Tests. Journal of Financial Economics, 79, 257-292.
[26] Kuang.. &Davison (2017). Learning Word Embeddings with Chi-Square Weights for Healthcare Tweet Classification. - 2017/08/17.10.3390/app7080846.
[27] Azar, A. T., & El-Metwally, S. M. (2013). Decision tree classifiers for automated medical diagnosis. Neural Computing and Applications, 23, 2387-2403.
[28] Yao, J. G., Weasner, B. M., Wang, L. H., Jang, C. C., Weasner, B., Tang, C. Y., Salzer, C. L., Chen, C. H., Hay, B., Sun, Y. H., Kumar, J. P. (2008). Differential requirements for the Pax6 (5a) genes eyegone and twin of eyegone during eye development in Drosophila. Dev. Biol. 315 (2): 535--551.
[29] A, Rehman., Abbas, A. Chandio., I. Hussain., L, Jingdong, (2019). Fertilizer consumption, water availability and credit distribution: Major factors affecting agricultural productivity in Pakistan, Journal of the Saudi Society of Agricultural Sciences, Volume 18, Issue 3, 2019, Pages 269-274.
[30] Njoroge, p. (2016). Factors Influencing Uptake of Contraceptive Services Among Undergraduate Students aged 18-35 years at jomo kenyatta university of agriculture and technology, kenya. A Master Thesis in Public Health in the Jomo Kenyatta University Of Agriculture and Technology.
[31] Priyam, A., Abhijeeta, G. R., Rathee, A., & Srivastava, S. (2013). Comparative analysis of decision tree classification algorithms. International Journal of current engineering and technology, 3 (2), 334-337.
[32] Kithuka, B (2012). Factors Associated with Condom Use among Students at Jomo Kenyatta University of Agriculture and Technology. A Master of science in Epidemiology in the Jomo Kenyatta University of Agriculture & Technology.
Cite This Article
  • APA Style

    Sammy Kiprop, Charity Wamwea, Herbert Imboga, Joel Chelule. (2023). Classification of Contraceptive Use Among Undergraduate Students Using a Supervised Machine Learning Technique . American Journal of Theoretical and Applied Statistics, 12(5), 120-128. https://doi.org/10.11648/j.ajtas.20231205.14

    Copy | Download

    ACS Style

    Sammy Kiprop; Charity Wamwea; Herbert Imboga; Joel Chelule. Classification of Contraceptive Use Among Undergraduate Students Using a Supervised Machine Learning Technique . Am. J. Theor. Appl. Stat. 2023, 12(5), 120-128. doi: 10.11648/j.ajtas.20231205.14

    Copy | Download

    AMA Style

    Sammy Kiprop, Charity Wamwea, Herbert Imboga, Joel Chelule. Classification of Contraceptive Use Among Undergraduate Students Using a Supervised Machine Learning Technique . Am J Theor Appl Stat. 2023;12(5):120-128. doi: 10.11648/j.ajtas.20231205.14

    Copy | Download

  • @article{10.11648/j.ajtas.20231205.14,
      author = {Sammy Kiprop and Charity Wamwea and Herbert Imboga and Joel Chelule},
      title = {Classification of Contraceptive Use Among Undergraduate Students Using a Supervised Machine Learning Technique
    
    	
    },
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {12},
      number = {5},
      pages = {120-128},
      doi = {10.11648/j.ajtas.20231205.14},
      url = {https://doi.org/10.11648/j.ajtas.20231205.14},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajtas.20231205.14},
      abstract = {The Kenyan government in partnership with other stakeholders involved in providing family planning services have put in place various strategies and policies to increase uptake of contraceptives. This results in an increase in contraceptive prevalence rate (CPR), reduction of both total fertility rate (TFR) and sexually transmitted infections (STIs). Despite the various strategies and policies, the total fertility rate still remains high, while CPR has been unattained, respectively. The aim of this study was to classify contraceptives use among undergraduate students using a supervised machine learning technique. The target population constituted students at Jomo Kenyatta University of Agriculture and Technology (JKUAT) (Eldoret Campus). The study applied simple random sampling technique to obtain data from a sample of 252 using structured questionnaires. A decision tree classifier based on CHAID and C5.0 algorithms were used for classification. Pearson Chi-Squared statistic was used as feature selection technique to rank significant factors influencing contraceptives use based on their Chi scores. The findings show that the use of Chi-Squared feature selection led to contraceptives factors that were ranked higher having higher classification performance. The fitted decision tree model based on CHAID algorithm had a higher classification accuracy of 64.68% with 195 correct classifications as compared to the C5.0 decision tree model with accuracy of 61.18% with 163 correct classifications. The study findings contribute to a better insight on the classifications of contraceptives use among undergraduate students in Kenya. Hence, the government of Kenya can implement policies to enhance contraceptives awareness.
    },
     year = {2023}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Classification of Contraceptive Use Among Undergraduate Students Using a Supervised Machine Learning Technique
    
    	
    
    AU  - Sammy Kiprop
    AU  - Charity Wamwea
    AU  - Herbert Imboga
    AU  - Joel Chelule
    Y1  - 2023/10/28
    PY  - 2023
    N1  - https://doi.org/10.11648/j.ajtas.20231205.14
    DO  - 10.11648/j.ajtas.20231205.14
    T2  - American Journal of Theoretical and Applied Statistics
    JF  - American Journal of Theoretical and Applied Statistics
    JO  - American Journal of Theoretical and Applied Statistics
    SP  - 120
    EP  - 128
    PB  - Science Publishing Group
    SN  - 2326-9006
    UR  - https://doi.org/10.11648/j.ajtas.20231205.14
    AB  - The Kenyan government in partnership with other stakeholders involved in providing family planning services have put in place various strategies and policies to increase uptake of contraceptives. This results in an increase in contraceptive prevalence rate (CPR), reduction of both total fertility rate (TFR) and sexually transmitted infections (STIs). Despite the various strategies and policies, the total fertility rate still remains high, while CPR has been unattained, respectively. The aim of this study was to classify contraceptives use among undergraduate students using a supervised machine learning technique. The target population constituted students at Jomo Kenyatta University of Agriculture and Technology (JKUAT) (Eldoret Campus). The study applied simple random sampling technique to obtain data from a sample of 252 using structured questionnaires. A decision tree classifier based on CHAID and C5.0 algorithms were used for classification. Pearson Chi-Squared statistic was used as feature selection technique to rank significant factors influencing contraceptives use based on their Chi scores. The findings show that the use of Chi-Squared feature selection led to contraceptives factors that were ranked higher having higher classification performance. The fitted decision tree model based on CHAID algorithm had a higher classification accuracy of 64.68% with 195 correct classifications as compared to the C5.0 decision tree model with accuracy of 61.18% with 163 correct classifications. The study findings contribute to a better insight on the classifications of contraceptives use among undergraduate students in Kenya. Hence, the government of Kenya can implement policies to enhance contraceptives awareness.
    
    VL  - 12
    IS  - 5
    ER  - 

    Copy | Download

Author Information
  • Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

  • Section