Analysis of a platform to support the diagnosis of polycystic ovary syndrome
DOI:
https://doi.org/10.36825/RITI.12.27.008Keywords:
Artificial Intelligence, Data Mining, Classification Techniques, Women's Health, Polycystic Ovary SyndromeAbstract
Polycystic Ovary Syndrome (PCOS) is a common endocrine condition among women of reproductive age and its early diagnosis prevents long-term complications; however, this diagnosis presents difficulties due to the variability of symptoms, cultural taboos and the need for laboratory and imaging tests interpreted by specialists in Gynecology. On the other hand, Artificial Intelligence (AI) techniques have shown great potential to help in the early detection of different diseases. Currently, women in rural and semi-rural areas have little access to specialists and studies, so it is considered useful to have a health platform, which includes AI techniques, in mobile and web environments, that supports the detection of PCOS. This article shows the result of the systems analysis for the aforementioned platform, presenting the functional and non-functional requirements compiled with the help of health professionals in General Medicine and Gynecology.
References
Consejo Nacional de Población. (2022). Día Internacional de Acción por la Salud de las Mujeres. https://www.gob.mx/conapo/articulos/dia-internacional-de-accion-por-la-salud-de-las-mujeres-303826?idiom=es
Cámara de Diputados. (2017). Entre 6 y 10 por ciento de las mexicanas padece Síndrome del Ovario Poliquístico. http://www5.diputados.gob.mx/index.php/esl/Comunicacion/Boletines/2017/Julio/31/3888-Entre-6-y-10-por-ciento-de-las-mexicanas-padece-Sindrome-del-Ovario-Poliquistico
Corona Vázquez, T., Medina Mora, M. E., Ostrosky Wegman, P., Sarti Gutiérrez, E. J., Uribe Zúñiga, P. (2014). La mujer y la salud en México. Intersistemas S.A de C.V.
Mayo Clinic. (2023). Síndrome de ovario poliquístico. https://www.mayoclinic.org/es/diseases-conditions/pcos/symptoms-causes/syc-20353439
Teede, H., Tay, C. T., Laven, J. S. E., Dokras, A., Moran, L. J., Piltonen, T. (2023). International evidence-based guideline for the assessment and management of Polycystic Ovary Syndrome 2023. Monash University. https://doi.org/10.26180/24003834.V1
Vanhauwaert, P. S. (2021). Síndrome de ovario poliquístico e infertilidad. Revista Médica Clínica Las Condes, 32 (2), 166–172. https://doi.org/10.1016/j.rmclc.2020.11.005
Food and Travel. (2020). Apps para el cuidado femenino. https://foodandtravel.mx/apps-para-el-cuidado-femenino/.
Instituto Nacional de Estadística y Geografía. (2018). Encuesta Nacional de la Dinámica Demográfica (ENADID). https://www.inegi.org.mx/programas/enadid/2018/
MedlinePlus. (2022). Salud de las mujeres. https://medlineplus.gov/spanish/womenshealth.html
Concha, F., Sir, T., Recabarren, S. E., Pérez, F. (2017). Epigenética del síndrome de ovario poliquístico. Revista médica de Chile, 145 (7), 907–915. https://doi.org/10.4067/s0034-98872017000700907
Hamet, P., Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism, 69, S36–S40. https://doi.org/10.1016/j.metabol.2017.01.011
Buulolo, E., Kom, S., Kom, M. (2020). Data Mining Untuk Perguruan Tinggi. Deepublish.
Kantardzic, M. (2019). Data Mining: Concepts, Models, Methods, and Algorithms (3ra Ed.). Wiley-IEEE Press.
Cognizant. (2024). Plataforma digital. https://www.cognizant.com/es/es/glossary/digital-platform
GCFGlobal. (2024). Informática Básica: ¿Qué es una aplicación móvil? https://edu.gcfglobal.org/es/informatica-basica/que-es-una-aplicacion-movil/1/
Bharati, S., Podder, P., Hossain Mondal, M. R. (2020). Diagnosis of Polycystic Ovary Syndrome Using Machine Learning Algorithms. IEEE Region 10 Symposium (TENSYMP), Dhaka, Bangladesh. https://doi.org/10.1109/TENSYMP50017.2020.9230932
Elmannai, H., El-Rashidy, N., Mashal, I., Alohali, M. A., Farag, S., El-Sappagh, S., Saleh, H. (2023). Polycystic Ovary Syndrome Detection Machine Learning Model Based on Optimized Feature Selection and Explainable Artificial Intelligence. Diagnostics, 13 (8), 1-21. https://doi.org/10.3390/diagnostics13081506
Ramamoorthy, S., R., V., Sivasubramaniam, R. (2019). Monitoring the growth of Polycystic Ovary Syndrome using Mono-modal Image Registration Technique. ACM India Joint International Conference on Data Science and Management of Data, Kolkata, India. https://doi.org/10.1145/3297001.3297024
Fox, S. E., Menking, A., Eschler, J., Backonja, U. (2020). Multiples Over Models. ACM Transactions on Computer-Human Interaction, 27 (4), 1–24. https://doi.org/10.1145/3397178
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