American Journal of Data Mining and Knowledge Discovery

Submit a Manuscript

Publishing with us to make your research visible to the widest possible audience.

Propose a Special Issue

Building a community of authors and readers to discuss the latest research and develop new ideas.

Medical Factors-Based Mobile Application for Choosing Contraceptives: A Data Mining Approach

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.

Contraceptives, Data Mining, k-NN Algorithm, Support Vector Machine

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.

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

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Ahmad, P., Qamar, S. & Rizvi, S. (2015). Techniques of Data Mining In Healthcare: A Review. International Journal of Computer Applications. 120. 38-50. 10.5120/21307-4126.
2. Alano, A., & Hanson, L. (2018). “Women's perception about contraceptive use benefits towards empowerment: A phenomenological study in Southern Ethiopia”. 13 (9), PLoS ONE, e0203432.
3. Andrade C. (2021). The Inconvenient Truth About Convenience and Purposive Samples. Indian Journal of psychological medicine, 43 (1), 86–88.
4. Antonio, D., Sanchez, P. & Jose, A. O. (2018). Adolescent Contraceptive Use and its Effects on Fertility. V38, Article 45, Pages 1359–1388 DOI: 10.4054/DemRes.2018.38.45.
5. Ayyoubzadeh, S. M., Ghazisaeedi, M., Kalhori S. R. N., Hassaniazad M., Baniasadi T., Maghooli K., & Kahnouji K. (2020). A study of factors related to patient's length of stay using data mining techniques in a general hospital in southern Iran. Health Inf. Sci. Syst. doi: 10.1007/s13755-020- 0099-8.
6. Baker, R. S. J. d. (2010 in press). Data Mining for Education. McGaw, B., Peterson, P., Baker, E. (Eds.) International Encyclopedia of Education (3rd edition). Oxford, UK: Elsevier.
7. Carr L. (2021). FDA Approves New Oral Contraceptive Nextstellis. Retrieved on 2/2/2021 from
8. Chen, S. I., Tseng, H. T., & Hsieh C. C. (2020). Evaluating the impact of soy compounds on breast cancer using the data mining approach. Food Funct. 11: 4561–4570. doi: 10.1039/C9FO00976K.
9. Durowade, K. A., Omokanye, L. O., Elegbede, O. E., Adetokunbo, S., Olomofe, C. O., Ajiboye, A. D., Adeniyi, M. A., & Sanni, T. A. (2017). Barriers to Contraceptive Uptake among Women of Reproductive Age in a Semi-Urban Community of Ekiti State, Southwest Nigeria. Ethiopian Journal of health sciences, 27 (2), 121–128.
10. Ghiya, N., Godbole, S., Hol, P., Deortare, G. & Chavan, M. (2015) "Forecasting Diseases by Classification and Clustering Techniques", International Journal of Innovative Research in Science, Engineering, and Technology, 4 (1), pp. 18958-18961.
11. Han, J., Pei, J., & Kamber, M. (2012). Data Mining: Concepts and Techniques. Morgan Kaufmann publisher: USA, 3rd ed.
12. Hassan, R., Al-Insaif, S., Hossain, M. I., & Kamruzzaman, J. (2020). A Machine Learning Approach for Prediction of Pregnancy Outcome following IVF Treatment. Neural Computing and Applications DOI: 10.1007/s00521-018-3693-9.
13. Haq, I., Hossain, I., Rahman, M., Methun, I. H, Talukder, A., Jakaria Habib, J & Hossain, S., (2022). Contraceptive Practice among Ever-Married Women in Bangladesh: A Hierarchical Machine Learning Classification Approach. DOI:
14. Hessami, A. R., Sun, D., Odreman, G. J., Zhou, X., Nejat, A., & Saeedi, M. (2017). Project Scoping Guidebook for Metropolitan Planning Organization Transportation Projects. Kingsville, Texas: Texas A&M University Kingsville (TAMUK).
15. Jaitley, U. (2018). Why Data Normalization is necessary for Machine Learning models. Retrieved on 2/2/2022 from
16. Jothi, N. & Husain, W. (2015). Data mining in healthcare: A review. Procedia Comput. Sci. 2015; 72: 306–313. doi: 10.1016/j.12.145. [CrossRef] [Google Scholar].
17. Mayne pharma. (2021). U.S. FDA Approves NEXTSTELLIS®, New Oral Contraceptive. Retrieved on 2/2/2022 from
18. Metcalf, E. (2021). Birth Control Implants. Retrieved on 5/2/2022 from safety-side- effects.
19. Musa, M., Y., Wajiga, M. G. & Garba, E. J. (2019). Multi-Agent-Based Diagnostic Model for Breast Tumour Classification. American Journal of Data Mining and Knowledge Discovery. 4. 1-7. 10.11648/j.ajdmkd.20190401.11.
20. Nikolopoulou, K. (2022). What Is Purposive Sampling? | Definition & Examples. Available at
21. Pika A., Wynn, M. T., Budiono, S., Ter Hofstede, A. H., van der Aalst, W. M.,& Reijers H. A. (2020). Privacy- Preserving Process Mining in Healthcare. Int. J. Environ. Res. Public Health.; 17: 1612. doi: 10.3390/ijerph17051612.
22. Ramageri, B. M. (2010). Data Mining Techniques And Applications. Indian Journal of Computer Science and Engineering 1 (4) 301-305. ISSN: 0976- 5166.
23. Rahman, A., Honan B., Glanville T., Hough P., & Walker K. (2019). Using data mining to predict emergency department length of stay greater than 4 hours: Derivation and single-site validation of a decision tree algorithm. Emerg. Med. Australas. 32: 416–421. doi: 10.1111/1742- 6723.13421.
24. Reddy, R. P., Mandakini, C. h. & Radhika, C. H. (2020). A Review on Data Mining Techniques and Challenges in Medical Field. International Journal of Engineering Research & Technology (IJERT), 9 (8).
25. Ricciardi, C., Amboni, M., de Santis, C., Improta, G., Volpe G., Iuppariello L., Ricciardelli G., D’Addio G., Vitale, C., Barone P., et al. (2019). Using gait analysis parameters to classify Parkinsonism: A data mining approach. Comput. Methods Programs Biomed. 2019; 180: 105033. doi: 10.1016/j.cmpb.2019.105033.
26. Rustagi, N. & Taneja, D. K., Kaur, R. & Ingle, G. K.. (2010). Factors affecting contraception among women in a minority community in Delhi: A qualitative study. 33. 10-15.
27. Saeed, S., Shaik, A.& Memon, M. A. (2018). Impact of Data Mining Techniques To Analyze Health Care Data. Journal of Medical Imaging and Health Informatics. 8 (4): 682-690. DOI: 10.1166/jmihi.2018.2385.
28. Shouman, M., Turner, T., & Stocker, R. (2012). Applying k-Nearest Neighbour in Diagnosing Heart Disease Patients. International Journal of Information and Education Technology, V 2 (3).
29. Ţăranu I. (2016). Data mining in healthcare: Decision making and precision. Database Syst. 6: 33–40.
30. Urbaniak, A. (1996). Expert system for diagnosis of women's menstrual cycle using natural family planning method. Presented at the 17th IFIP Conference on System ModeUing and Optimization.
31. Yim, S. J., Lui L. M., Lee Y., Rosenblat, J. D., Ragguett R.-M., Park C., Subramaniapillai M., Cao B., Zhou A., & Rong C., et al (2020). The utility of smartphone-based, ecological momentary assessment for depressive symptoms. J. Affect. Disord. 2020; 274: 602–609. doi: 10.1016/j.jad.2020.05.116.