SMARTCAREHUB: DEEP LEARNING-BASED VIRTUAL HEALTH CONSULTING SYSTEM FOR PERSONALIZED PATIENT SUPPORT

Abstract

The exponential growth of digital healthcare and telemedicine has created an urgent need for intelligent systems that can assist patients remotely with accurate medical guidance. Traditional online health consulting systems rely primarily on rule-based algorithms and static databases, limiting their ability to provide personalized and context-aware recommendations. To address this gap, this paper proposes SmartCareHub, a Deep Learning-Based Virtual Health Consulting System designed to deliver accurate, real-time medical consultations tailored to individual patient profiles. The system integrates natural language processing (NLP) for understanding patient symptoms, convolutional neural networks (CNNs) for medical image analysis, and recommendation models for suggesting appropriate treatments or specialist consultations. SmartCareHub not only improves diagnostic accuracy but also enhances accessibility for rural and underserved populations through web-based and mobile interfaces. The model uses historical medical data, symptom patterns, and user feedback to continuously refine its recommendations via reinforcement learning. Experimental evaluations on publicly available medical datasets demonstrate that SmartCareHub achieves superior accuracy and reliability compared to traditional decision-tree and logistic regression-based health advisory systems. Overall, SmartCareHub represents a major step forward toward AI-driven personalized healthcare, providing patients with faster, data-supported, and affordable medical assistance.