Rural women face a high prevalence of chronic diseases such as Anemia, PCOS, thyroid disorders, and diabetes, with limited access to early screening and continuous monitoring. This paper proposes HealthyHer, a low-cost healthcare system designed for early risk assessment and continuous health tracking. The system adopts a dual-model approach by categorizing users into fresh users and existing patients. A Random Forest-based classification method is incorporated to analyze symptoms and predict risk levels. Based on the results, the system provides structured recommendations including medical consultation, diagnostic tests, and dietary guidance. The system also supports integration with Primary Health Centre’s (PHCs) to enable better follow-up and data management. The proposed solution is scalable, affordable, and suitable for deployment in low-resource rural environments.

This paper presents HealthyHer, a scalable and cost- effective healthcare solution designed to address the challenges of early detection and monitoring of chronic diseases among rural women. By incorporating a dual-model approach and a machine learning-based risk classification mechanism, the system provides structured and practical healthcare support. The proposed solution has strong potential for real- world implementation and can significantly improve healthcare accessibility and outcomes in rural communities.

1.World Health Organization, “Women’s Health and Rural Healthcare,” Geneva, Switzerland, 2022. 2.Government of India, “National Family Health Survey (NFHS-5),” Ministry of Health and Family Welfare, 2021. 3.Ministry of Health and Family Welfare, “Rural Health Statistics,” New Delhi, India, 2023. 4.L. Breiman, “Random Forests,” Machine Learning Journal, vol. 45, no. 1, pp. 5–32, 2001.

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