Towards Safer Water: AI-Driven Predictive Analytics for Disease Detection

Water quality is a critical factor for human health and environmental sustainability. Rapid urbanization and industrialization have led to significant water contamination, increasing the prevalence of waterborne diseases. This study investigates the presence of pathogens in water sources across the Gujarat region, utilizing machine learning models to analyze contamination patterns. Various classifiers, including HistGradientBoosting, Random Forest, AdaBoost, Bagging, Decision Tree, and LSTM, were employed to predict water quality and identify pathogens. Among these, the Random Forest and Bagging classifiers exhibited the highest accuracy at 98.53%. Furthermore, Explainable AI techniques, specifically SHapley Additive exPlanations (SHAP), were used to interpret the significant features influencing contamination levels. The study highlights the need for proactive water quality monitoring and pathogen detection to prevent disease outbreaks.