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.
| Published in | American Journal of Theoretical and Applied Statistics (Volume 12, Issue 5) |
| DOI | 10.11648/j.ajtas.20231205.14 |
| 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 |
Chi-Squared Feature Selection, Decision Tree Classifier, CHAID Algorithm, C5.0 Algorithm, Contraceptive’s Use
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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
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
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
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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://article.sciencepublishinggroup.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}
}
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 -
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