A Novel Approach for Identification and Monitoring of Critical Cancer Cases Using a Multi-Agent System

Recent research in cancer detection and monitoring is based on the development of multi-agent systems. They are used for multidimensional multimodal health data integration, medical data augmentation, knowledge representation, predictive diagnosis, and personalized treatment schemes. This paper addresses the last two challenges by introducing intelligent agents to build clustering, classification, and treatment-recommendation models, while also improving overall process time through feature selection and the identification of critical malignant cases. In the first stage, the Wrapper Selection Agent based on Random Forests generated an optimized model with a 98.68% accuracy. Then, the Outlier-based Clustering and Critical Malignant Cases Agents detected the critical malignant cases with a 0.84 Silhouette Score. In the next step, Treatment Clustering and Decision Rules Agents built a perfect model that proposes a personalized treatment for the patients identified by the previous agents. The entire process is automated and provides treatment recommendations in 32.85 seconds.