AI-based conceptual framework medical waste management and prediction model proposal: Türkiye analysis (2018-2022)

Abstract

Aim: This study aimed to analyze the trends and characteristics of medical waste generation in Türkiye between 2018 and 2022 and to propose an artificial intelligence (AI)-based prediction model that integrates medical waste data with healthcare service indicators for improved waste management efficiency.

Materials and Methods: The medical waste data were obtained from the Turkish Statistical Institute (TURKSTAT) based on waste statistics for the years 2018-2022. In addition, healthcare service indicators (such as hospital bed capacity, bed occupancy rate, and patient volume) were examined to assess their potential relationship with waste generation. Time-series and regional analyses were conducted to evaluate changes in waste production and the distribution of disposal methods. Based on these findings, a conceptual artificial intelligence forecasting framework was proposed, which could utilize machine learning algorithms such as Random Forest and LSTM to estimate future medical waste quantities.

Results: Preliminary analyses indicated a consistent increase in medical waste generation, with higher concentrations observed in metropolitan regions such as İstanbul, Ankara, and İzmir. Sterilization and controlled landfill were found to be the dominant disposal methods. The proposed AI model framework demonstrated high potential in forecasting waste generation by combining service intensity and demographic factors.

Conclusions: Integrating AI-based predictive modeling with medical waste data and healthcare indicators can significantly improve the planning, optimization, and sustainability of waste management systems in healthcare institutions. Future studies incorporating real-time IoT and environmental data are recommended to enhance predictive accuracy and operational efficiency.