Predictive Modeling of Hospital Waste Generation Using Machine Learning Based on Patient Inflow

Muhammad Arshad; Muhammad Umer Farooq; Najeeb Ullah Khan; Kifayat Ullah; Bilal Ur Rehman; Salman Ilahi Siddiqui; Muhammad Farooq; Muhammad Kashif Khan

Authors

Muhammad Arshad Department of Industrial Engineering, Jalozai Campus, UET Peshawar, Pakistan
Muhammad Umer Farooq Department of Industrial Engineering, Jalozai Campus, UET Peshawar, Pakistan
Najeeb Ullah Khan Department of Industrial Engineering, Jalozai Campus, UET Peshawar, Pakistan
Kifayat Ullah Department of Electrical Engineering, UET Peshawar
Bilal Ur Rehman Department of Electrical Engineering, UET Peshawar
Salman Ilahi Siddiqui Department of Electrical Engineering, UET Peshawar
Muhammad Farooq Department of Electrical Engineering, UET Peshawar
Muhammad Kashif Khan Department of Electrical Engineering, UET Peshawar

Keywords:

Predictive Modeling, Hospital Waste Estimation, Linear Regression, Patient Inflow, ANOVA Validation, Healthcare Forecasting, Waste Management Optimization

Abstract

Effective hospital waste management is a key to the security of the environment and provision of healthcare hygiene. This paper develops a predictive analytics model to forecast the amount of daily hospital waste generated based on patient inflow using a linear regression model. Real-time data from 60 days were gathered in a tertiary healthcare institution, which informed the number of patients and the resultant waste in kilograms. The model obtained the R value of 0.88 in the training and 0.81 in the validation datasets and a Root Mean Square Error (RMSE) of 130.52 kg. The predictor of patient volume was established as significant through statistical validation via ANOVA, and the model was found to be within the key regression assumptions through the residual analysis. The findings emphasize that predictive modeling within a hospital waste planning system is viable, and a cost-efficient and explainable option can be used in operational forecasting. The offered method contributes to enhanced resource distribution, risk aversion, and adherence to the sustainable healthcare objectives.

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