Integrating Data Envelopment Analysis and Machine Learning for Resource Allocation in Efficient Multitumor Analyzer for Brain Tumors

Abstract

The Efficient Multitumor Analyzer for segmentation and classification of brain tumors, while promising, faces several drawbacks that limit its effectiveness in clinical settings. A framework that combines Data Envelopment Analysis (DEA) with Machine Learning (ML) approaches is presented in the proposed research to enhance decision-making in healthcare resource allocation, specifically within the context of deploying an Efficient Multitumor Analyzer for brain tumor segmentation and classification. DEA assesses the efficiency of healthcare providers based on inputs such as staffing, equipment, and budget, and outputs like treatment outcomes and patient satisfaction. After this evaluation, ensemble techniques and machine learning algorithms like Random Forests and Gradient Boosting, analyze factors influencing efficiency and predict resource needs for implementing the Multitumor Analyzer. The model achieved a prediction accuracy of 98.87% in identifying potential resource shortages, enabling proactive management of brain tumor care services.