Forecast of Clinical Waste using Trendline Linear and Moving Average

Abstract

The management of medical waste has become an increasingly pressing issue. The forecasting of medical waste generation is crucial for effective resource allocation and waste management strategies. However, the forecasting value of medical waste generated remains uncertain due to the complex and dynamic nature of healthcare facilities. By exploring the Trendline Linear method, which leverages linear regression to model the relationship between time and waste generation, and the Moving Average method, a time series analysis technique, this study provides the forecasting value of clinical waste generated in 2023 and to address this gap by evaluating the effectiveness of two forecasting methods by using performance accuracy Mean Absolute Error (MAE) in predicting the generation of clinical waste. By doing so, it seeks to provide insights into the reliability and accuracy of forecasting methods in the context of clinical waste management, ultimately contributing to the development of more efficient and sustainable waste management practices in healthcare settings. Further research is encouraged to explore the applicability of these methods in diverse healthcare settings and to investigate additional factors that may influence medical waste generation patterns. Further research is encouraged to explore the applicability of these methods in diverse healthcare settings and to investigate additional factors that may influence medical waste generation patterns. The study seeks to provide insights into the reliability and accuracy of the two forecasting methods in the context of clinical waste management. The performance of the methods is evaluated using the Mean Absolute Error (MAE) metric to assess their ability to predict the generation of clinical waste.