Advancing allergy source mapping: A comprehensive multidisciplinary framework integrating machine learning, graph theory and game theory

Abstract

Allergic reactions can range from mild discomfort to life-threatening situations. To manage the healthcare difficulty, an efficient allergens mapping is required by mapping the allergies to reduce the severity risk reactions. The allergies are mapping with specific food items according to the daily usage. It enables the clear communication, targeted avoidance, and public awareness, which is necessary to scrutinize the prevention process. Since, the allergies are highly individualized and can exhibit significant variability among individuals that are capturing the complexity in the process of overcoming the predictive challenges. The complex relationships and data challenges require advanced approaches like ML and graph theory. For this purpose, we propose a new multidisciplinary framework that integrates the Machine Learning (ML), Graph Theory and Game Theory to predict the allergies associated with relevant foods using a modest dataset. This framework has two newly built graph models such as a conventional approach and a refined approach, to pave the way for better results. Here, the ML techniques are employed to perform the classification process on tabular data that are observed the remarkable improvements and transforming the data into a graph. Moreover, the Darwinian decision-making framework is adopted theoretically in evolutionary game theory to formulate effective formulas for assessing the spread of allergies among allergens and predict the allergies dynamically. The proposed framework has been evaluated by conducting experiments by using a modest dataset by considering the evaluation metrics such as accuracy, macro-precision, macro-recall, and macro F1-score.