Classification of Infertility Risk in Female Patients Based on Medical Record Data Using Naive Bayes Algorithm
Article Sidebar
Main Article Content
Abstract
Infertility is a reproductive health problem that has a significant impact globally, especially in developing countries such as Indonesia. This study aims to classify the risk of infertility in female patients at Rantauprapat Regional Hospital by utilizing the Naive Bayes algorithm based on electronic medical record data. The data used consisted of 500 medical records of female patients of childbearing age during the period 2019–2022, which had been processed and divided into training data (70%) and testing data (30%). The analysis and modeling process was carried out using the RapidMiner application without requiring programming skills. The results showed that the Naive Bayes model was able to classify the risk of infertility with an accuracy level of 86.7%, precision of 91.0%, recall of 93.2%, and F1-score of 92.1%. The main factors that most influence the classification of infertility include a history of reproductive disease, patient age, hormonal examination results, body mass index, and history of sexually transmitted infections. These findings indicate that the integration of the Naive Bayes algorithm into medical record data can be an effective solution for early detection of infertility clinically and support data-based decision making. This study also recommends increasing data and attribute coverage, as well as comparison with other algorithms for more optimal results in the future
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
A. Otoom and E. E. Abdallah, “Breast cancer diagnosis system based on Naive Bayes classifier,” International Journal of Computer Applications, vol. 99, no. 10, pp. 13–17, 2014.
A. Jayanthi and S. Subashini, “Classification of Diabetes Disease Using Support Vector Machine,” International Journal of Computer Applications, vol. 62, no. 12, pp. 11–14, 2013.
A. F. Butts and C. D. Shaughnessy, “Environmental and lifestyle risk factors for infertility,” Best Practice & Research Clinical Obstetrics & Gynaecology, vol. 35, pp. 3–10, 2016.
B. Aldosari, Health Informatics: Practical Guide for Healthcare and Information Technology Professionals, 7th ed., Lulu.com, 2017.
C. J. Cousineau and A. Domar, “Psychological impact of infertility,” Best Practice & Research Clinical Obstetrics & Gynaecology, vol. 21, no. 2, pp. 293–308, 2007World Health Organization, “Infertility,” 2023. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/infertility [Accessed: 29-Apr-2024].
D. Novitasari, S. Setyawati, and F. Ramadani, “Pemanfaatan Rekam Medis Elektronik untuk Kesehatan Reproduksi,” Jurnal Kesehatan Masyarakat, vol. 14, no. 2, pp. 121–128, 2019.
G. M. Joffe, “Lifestyle and environmental contributions to male infertility,” BMJ, vol. 336, no. 7644, pp. 1403–1404, 2008.
H. Zhang and C. X. Ling, “An improved learning algorithm for naive Bayes,” in Proc. 17th Int. Joint Conf. Artif. Intell., 2001, pp. 903–908.
H. Kaur and S. K. Wasan, “Empirical Study on Applications of Data Mining Techniques in Healthcare,” Journal of
J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed., Morgan Kaufmann, 2012.
M. A. Fritz and L. Speroff, Clinical Gynecologic Endocrinology and Infertility, 8th ed., Philadelphia: Lippincott Williams & Wilkins, 2011.
M. N. Mascarenhas, S. R. Flaxman, T. Boerma, S. Vanderpoel, and G. Stevens, “National, regional, and global trends in infertility prevalence since 1990: a systematic analysis of 277 health surveys,” PLoS Med., vol. 9, no. 12, p. e1001356, 2012.
M. T. O. Widyaningsih and E. N. Paramita, “Analisis Data Rekam Medis Elektronik untuk Prediksi Risiko Kehamilan Menggunakan Data Mining,” Jurnal Kesehatan Reproduksi, vol. 11, no. 2, pp. 123–130, 2020N. S. Checa Vizcaíno, F. Vassena, and S. Rodríguez, “Infertility: impact of lifestyle and environmental factors,” Gynecological Endocrinology, vol. 34, no. 7, pp. 473–477, 2018.
Practice Committee of the American Society for Reproductive Medicine, “Diagnostic evaluation of the infertile female: a committee opinion,” Fertil. Steril., vol. 103, no. 6, pp. e44–e50, 2015.
S. W. Nugroho, R. Astuti, and N. K. Yuniar, “Prevalensi Infertilitas di Indonesia,” Jurnal Kesehatan Reproduksi, vol. 8, no. 2, pp. 95–102, 2017.
W. Ombelet, J. Cooke, J. Dyer, Z. Serour, and G. Devroey, “Infertility and the provision of infertility medical services in developing countries,” Hum. Reprod. Update, vol. 14, no. 6, pp. 605–621, 2008.
Perkumpulan Obstetri dan Ginekologi Indonesia (POGI), “Pedoman Nasional Pelayanan Kedokteran Tata Laksana Infertilitas,” 2022.