METHODOLOGY FOR APPLYING AI-BASED ANALYTICAL SYSTEMS IN HYGIENE AND MEDICAL ECOLOGY
Keywords:
Hygiene; Medical Ecology; Artificial Intelligence (AI); Machine Learning; Neural Networks; Ecological Monitoring; Big Data; Forecasting.Abstract
This article develops and evaluates a methodology for using artificial intelligence (AI)-based analytical systems in the field of Hygiene and Medical Ecology. During the study, large datasets on air quality, water quality, soil composition, and population health were processed using AI algorithms, revealing complex relationships between environmental factors and diseases. The results showed that AI-based neural networks improved prediction accuracy by 17–20% compared to traditional statistical methods, and in water resource monitoring, predicted the risk of microbiological contamination with 92% accuracy. Forecasts suggest that by 2030, AI-based ecological monitoring systems could increase early disease detection by 25–30%, and by 2035, reduce premature deaths related to air pollution by 1.2 million. This methodology is considered a strategic tool for scientifically substantiating health policy, early identification of ecological risks, and protecting public health.
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