PREDICTIVE HYGIENE FOR HEALTH MONITORING AND RISK REDUCTION USING MACHINE LEARNING

Abstract Hygiene is an important aspect in maintaining public health, particularly in areas prone to disease transmission, such as hospitals, schools, and public facilities. Traditional hygiene monitoring systems are frequently manual and reactive, resulting in slow responses and 5 increased health hazards. Predictive hygiene uses machine learning (ML) to provide realtime health
monitoring and early danger detection. ML systems like Random Forest and Support Vector Machines can detect hygiene breaches and predict probable contamination incidents by processing data from Io T sensors, environmental factors, and behavioural patterns. This proactive method allows for quick interventions, which improves hygiene 10 compliance and lowers infection rates. The system continuously learns from historical and real-time data to adapt to changing hygiene dynamics, resulting in better decision-making. Predictive hygiene, with its ability to recognise trends and forecast results, enables scalable and effective health risk management. Integrating ML-driven hygiene solutions helps to move public health efforts towards preventive,data-driven approaches in clinical and 15 community settings.