Healthcare accessibility remains a significant challenge for rural women due to limited medical facilities, lack of awareness, and socio-economic constraints. Early identification of health risks can significantly improve preventive healthcare and reduce disease burden. This paper proposes an Artificial Intelligence (AI)-driven mobile health monitoring system designed specifically for rural women. The system collects health-related data through a mobile application and analyzes it using machine learning algorithms to predict potential health risks. Key parameters such as age, body mass index (BMI), blood pressure, glucose level, hemoglobin level, and lifestyle habits are considered for predictive analysis. Machine learning algorithms including Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine are implemented and evaluated. Experimental results show that the Random Forest model achieves the highest prediction accuracy. The proposed system enables early detection of health risks, improves healthcare awareness, and supports preventive healthcare strategies in rural communities.

This study presents an AI-driven mobile health monitoring system designed to predict health risks among rural women using machine learning techniques. The system demonstrates the potential of predictive analytics in improving rural healthcare services.

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