Published Paper Details:

Water-borne diseases remain a significant public health concern, particularly in regions where access to safe drinking water and sanitation facilities is limited. Contaminated water serves as a transmission medium for diseases such as cholera, typhoid, diarrhea, hepatitis A, dysentery, and giardiasis, resulting in severe health and economic impacts. This study presents a Smart Community Health Monitoring and Early Warning System for Water-Borne Diseases using machine learning techniques to estimate disease risk from water quality parameters. The system analyzes factors including temperature, pH, dissolved oxygen, turbidity, conductivity, total dissolved solids, and Water Quality Index to assess contamination levels and predict disease susceptibility. Data preprocessing, feature extraction, and multi-output classification techniques are employed to enhance prediction accuracy and reliability. Multiple machine learning algorithms, including Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, and XGBoost, are evaluated for performance comparison. An early warning mechanism generates alerts when high-risk conditions are detected, supporting timely preventive measures. Experimental results demonstrate effective risk prediction across multiple disease categories, indicating that the proposed framework can support proactive community health monitoring, disease prevention, and informed decision-making.