Mapping Health Pathways: A Network Analysis for Improved Illness Prediction

Abstract

Complex networks and network reconstruction have now become powerful tools for exploring relationships and interactions within systems across various fields to identify patterns and increase predictive accuracy. It provides a structured framework to investigate the relations or connections among individual components or entities within a system. Recently, the importance of the healthcare prediction model in life-saving efforts has increased. A model is proposed for health prediction that uses network reconstruction methods to build a network from available information, representing features as nodes and the relationships between them as edges. Subsequently, a method is introduced to calculate the value of the decision parameter ($\alpha$) for predicting an individual's health status. The proposed model shows substantial improvement over the current state of the prediction model. The first aim of the proposed model is to classify an individual into their appropriate class properly. Another contribution of the proposed model is to measure the factors that are responsible for classifying an individual into a class that shows its significance and impact over the existing state-of-the-art. It provides a new dimension to the prediction mode, emphasizing the importance of identifying critical features and their interdependencies for personalized health diagnostics.