Título: AI-Based Women’s Fertility and Period Tracking System
Autores: Hillary Saldanha, Andrey de Oliveira Sabino, Náthalee Cavalcanti de Almeida Lima & Rosana C. B. Rego
Resumo: The lack of robust and personalized systems for women’s health monitoring that integrate period data with AI-based analyses can compromise user experience and limit the impact of data in promoting health. This paper proposes the development of an application for women’s fertility monitoring and menstrual cycle tracking, using AI for data analysis and accurate predictions. The system integrates period data to provide personalized and useful insights for users. Grounded in machine learning theory applied to health data processing and analysis, this work utilizes AI to enhance the accuracy of data interpretation, tailoring recommendations and alerts to each user’s specific profile. The proposed algorithm proved effective in providing accurate menstrual cycle predictions with 100% accuracy. The primary contribution of this study is the development of an intelligent health tool that promotes autonomy and preventive care. This system may serve as a model for future AI applications in health monitoring, providing a solid foundation for a more personalized and precise approach to women’s health.
Palavras-chave: artificial intelligence; neural networks; machine learning; period tracking.
Páginas: 8
Código DOI: 10.21528/CBIC2025-1175297
Artigo em PDF: CBIC_2025_paper1175297.pdf
Arquivo BibTeX:
CBIC_2025_1175297.bib