HealthGuardian: Artificial Intelligence System for Early Diagnosis and Clinical Support

In today's fast-paced world, access to timely and accurate healthcare information is critical, yet many individuals face challenges in assessing their symptoms, finding appropriate care, and understanding potential treatments. To address these issues, we propose HealthGuardian, an AI-powered clinical decision support system designed to assist users with symptom checking, personalized treatment recommendations, healthcare provider suggestions, online consultations, and early-stage diabetes prediction. HealthGuardian allows users to input their symptoms through natural language processing (NLP) and a recur-rent neural network (RNN) model, providing possible diagnoses and advising when to seek medical attention. The system offers personalized treatment plans based on medical history, suggests nearby healthcare providers using the Overpass API, and facilitates online consultations with doctors. Additionally, it features a predictive tool for early-stage diabetes risk assessment using various machine learning algorithms. The system leverages machine learning algorithms such as logistic regression, decision tree classifier, Naive Bayes, and random forest classifier to enhance diagnostic accuracy and prediction reliability. Our results demonstrate that HealthGuardian improves early diagnosis accuracy, provides actionable treatment guidance, and increases healthcare accessibility, helping users make informed decisions about their health.