Rural women face significant challenges in accessing timely and quality healthcare due to geographical, economic, and social constraints. The lack of continuous health monitoring often leads to delayed diagnosis of preventable diseases. This paper proposes an AI-driven mobile health monitoring system designed to predict potential health risks among rural women using machine learning techniques. The system integrates mobile-based data collection with predictive analytics to analyze vital health parameters such as age, body mass index (BMI), blood pressure, glucose levels, and lifestyle factors. Multiple machine learning models, including Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine, are evaluated. Experimental results indicate that the Random Forest model achieves superior prediction accuracy. The proposed system enables early intervention, improves health awareness, and supports preventive healthcare for rural women.

This paper presents an AI-driven mobile health monitoring system aimed at improving healthcare outcomes for rural women. Machine learning-based predictive analytics enable early identification of health risks and support preventive care.

1.Esteva, A., Kuprel, B., Novoa, R. A., et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature (2017). 2.Rajkomar, A., Dean, J., Kohane, I. Machine Learning in Medicine. New England Journal of Medicine (2019). 3.Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine (2019). 4.Miotto, R., Li, L., Kidd, B. A., Dudley, J. T. Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records. Scientific Reports (2016). 5.Shickel, B., Tighe, P. J., Bihorac, A., Rashidi, P. Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis. IEEE Journal of Biomedical and Health Informatics (2018).