Global Trends in Medical Waste Management and Predictive Modeling of Water Quality Contamination
A Hybrid Approach Using Scimat and Neural Networks in Orange
DOI:
https://doi.org/10.59680/ishel.v3i1.2258Keywords:
Bibliometric Analysis, Environmental Health, Medical Waste Management, Neural Network Modeling, Water Quality ContaminationAbstract
The rapid growth of healthcare services has significantly increased the generation of medical waste, posing serious risks to environmental and public health, particularly through water quality contamination. This study examines global trends in medical waste management and proposes a hybrid analytical framework combining bibliometric analysis using SCIMAT and predictive modeling based on artificial neural networks implemented in Orange. Bibliometric mapping was employed to identify dominant research themes, temporal evolution, and knowledge gaps in medical waste and water contamination studies from 2000 to 2023. Subsequently, a neural network model was developed to predict potential water quality deterioration associated with mismanaged medical waste, using simulated environmental indicators. The results reveal a strong research focus on incineration, infection control, and hazardous waste, while predictive modeling of water contamination remains underexplored. The proposed hybrid approach demonstrates high predictive accuracy and offers a robust decision-support tool for environmental health policy. This study contributes methodologically and substantively to sustainable medical waste management and water resource protection.
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