Women's negative experience with contraception and understanding of the experience differentials, coupled with Limited information accessibility on contraceptives to healthcare centers, is responsible for the unwillingness and discontinuation in the use of contraceptives in Nigeria. The study aims at developing a Medical Factor based Mobile application Model for contraceptive implants using K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) techniques for the prediction of discomfort, and blood type. KNN and SVM techniques in R were the two methods used to develop a model to classify blood group types based on discomfort and vice versa. 10-fold cross-validation was carried out and was repeated 3 times and the optimal values were selected. The model was tested by the use of Predict function in which test data was used as the new data (input data) of the model. Experimental results showed the prediction accuracy of the KNN model was 85.72% and the SVM model was 92.2%. SVM outperformed KNN. However, the performances of models imply that the application can be used by women as the means for accessing information on discomforts associated with contraceptive implants as well as blood type. Most women with similar blood types have similar experiences (discomforts). Therefore this model can be used to choose the right contraceptive that is friendly to one blood type. The prediction mobile application of the tested model frontend was implemented in Android built-in with Java as the programming language. The backend was designed using structural query language in the WAMP server.
| Published in | American Journal of Data Mining and Knowledge Discovery (Volume 8, Issue 1) |
| DOI | 10.11648/j.ajdmkd.20230801.11 |
| Page(s) | 1-10 |
| 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 |
Contraceptives, Data Mining, k-NN Algorithm, Support Vector Machine
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APA Style
Bala Pwa’anda Bulus, Yusuf Musa Malgwi. (2023). Medical Factors-Based Mobile Application for Choosing Contraceptives: A Data Mining Approach. American Journal of Data Mining and Knowledge Discovery, 8(1), 1-10. https://doi.org/10.11648/j.ajdmkd.20230801.11
ACS Style
Bala Pwa’anda Bulus; Yusuf Musa Malgwi. Medical Factors-Based Mobile Application for Choosing Contraceptives: A Data Mining Approach. Am. J. Data Min. Knowl. Discov. 2023, 8(1), 1-10. doi: 10.11648/j.ajdmkd.20230801.11
AMA Style
Bala Pwa’anda Bulus, Yusuf Musa Malgwi. Medical Factors-Based Mobile Application for Choosing Contraceptives: A Data Mining Approach. Am J Data Min Knowl Discov. 2023;8(1):1-10. doi: 10.11648/j.ajdmkd.20230801.11
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Download@article{10.11648/j.ajdmkd.20230801.11,
author = {Bala Pwa’anda Bulus and Yusuf Musa Malgwi},
title = {Medical Factors-Based Mobile Application for Choosing Contraceptives: A Data Mining Approach},
journal = {American Journal of Data Mining and Knowledge Discovery},
volume = {8},
number = {1},
pages = {1-10},
doi = {10.11648/j.ajdmkd.20230801.11},
url = {https://doi.org/10.11648/j.ajdmkd.20230801.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20230801.11},
abstract = {Women's negative experience with contraception and understanding of the experience differentials, coupled with Limited information accessibility on contraceptives to healthcare centers, is responsible for the unwillingness and discontinuation in the use of contraceptives in Nigeria. The study aims at developing a Medical Factor based Mobile application Model for contraceptive implants using K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) techniques for the prediction of discomfort, and blood type. KNN and SVM techniques in R were the two methods used to develop a model to classify blood group types based on discomfort and vice versa. 10-fold cross-validation was carried out and was repeated 3 times and the optimal values were selected. The model was tested by the use of Predict function in which test data was used as the new data (input data) of the model. Experimental results showed the prediction accuracy of the KNN model was 85.72% and the SVM model was 92.2%. SVM outperformed KNN. However, the performances of models imply that the application can be used by women as the means for accessing information on discomforts associated with contraceptive implants as well as blood type. Most women with similar blood types have similar experiences (discomforts). Therefore this model can be used to choose the right contraceptive that is friendly to one blood type. The prediction mobile application of the tested model frontend was implemented in Android built-in with Java as the programming language. The backend was designed using structural query language in the WAMP server.},
year = {2023}
}
TY - JOUR T1 - Medical Factors-Based Mobile Application for Choosing Contraceptives: A Data Mining Approach AU - Bala Pwa’anda Bulus AU - Yusuf Musa Malgwi Y1 - 2023/07/24 PY - 2023 N1 - https://doi.org/10.11648/j.ajdmkd.20230801.11 DO - 10.11648/j.ajdmkd.20230801.11 T2 - American Journal of Data Mining and Knowledge Discovery JF - American Journal of Data Mining and Knowledge Discovery JO - American Journal of Data Mining and Knowledge Discovery SP - 1 EP - 10 PB - Science Publishing Group SN - 2578-7837 UR - https://doi.org/10.11648/j.ajdmkd.20230801.11 AB - Women's negative experience with contraception and understanding of the experience differentials, coupled with Limited information accessibility on contraceptives to healthcare centers, is responsible for the unwillingness and discontinuation in the use of contraceptives in Nigeria. The study aims at developing a Medical Factor based Mobile application Model for contraceptive implants using K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) techniques for the prediction of discomfort, and blood type. KNN and SVM techniques in R were the two methods used to develop a model to classify blood group types based on discomfort and vice versa. 10-fold cross-validation was carried out and was repeated 3 times and the optimal values were selected. The model was tested by the use of Predict function in which test data was used as the new data (input data) of the model. Experimental results showed the prediction accuracy of the KNN model was 85.72% and the SVM model was 92.2%. SVM outperformed KNN. However, the performances of models imply that the application can be used by women as the means for accessing information on discomforts associated with contraceptive implants as well as blood type. Most women with similar blood types have similar experiences (discomforts). Therefore this model can be used to choose the right contraceptive that is friendly to one blood type. The prediction mobile application of the tested model frontend was implemented in Android built-in with Java as the programming language. The backend was designed using structural query language in the WAMP server. VL - 8 IS - 1 ER -
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