Feature Selection and Extraction for Diabetes Management Using Bayesian Network-Based Emperor Penguins Colony Recommendation System

Abstract

In the face of the global diabetes epidemic, marked by a steep rise in cases from 1980 to 2021, novel strategies are imperative. This study introduces an innovative approach that synergizes Bayesian networks and the adaptability of Emperor Penguins Colonies to tackle diabetes through advanced feature selection and extraction. Diabetes, a complex disease with multifaceted causative factors, necessitates predictive measures that go beyond conventional methods. The proposed Recommendation System for Food and Exercise (RSFE) harnesses Bayesian network-based techniques to analyze health parameters and medical reports, generating personalized diet and exercise plans for individuals, including those with comorbid diabetes and blood pressure conditions. By mirroring the dynamic decision-making of Emperor Penguins Colonies, this approach aims to create an intelligent healthcare system that transforms intricate datasets into actionable insights, offering tailored recommendations for optimal health management. This research holds promise for proactive disease intervention and personalized care, aligning with the urgent need for effective diabetes management.