A Data Mining Approach for Health Transport Demand

Abstract

Efficient planning and management of health transport services are crucial for improving accessibility and enhancing the quality of healthcare. This study focuses on the choice of determinant variables in the prediction of health transport demand using data mining and analysis techniques. Specifically, health transport services data from Asturias, spanning a seven-year period, are analyzed with the aim of developing accurate predictive models. The problem at hand requires the handling of large volumes of data and multiple predictor variables, leading to challenges in computational cost and interpretation of the results. Therefore, data mining techniques are applied to identify the most relevant variables in the design of predictive models. This approach allows for reducing the computational cost without sacrificing prediction accuracy. The findings of this study underscore that the selection of significant variables is essential for optimizing medical transport resources and improving the planning of emergency services. With the most relevant variables identified, a balance between prediction accuracy and computational efficiency is achieved. As a result, improved service management is observed to lead to increased accessibility to health services and better resource planning.