Building an AI-Driven Symptom Checker Using Python Django for Enhanced Telemedicine Services

Abstract

Telemedicine and digital health technologies are revolutionizing healthcare delivery by enabling remote consultations, diagnosis and treatment in remote or far end regions and during global health crises. Building an AI enabled symptom checkers for clinical decision support system (CDSS) for enhanced telemedicine services serve as the first point of contact for patients for assessing symptom severity and reducing healthcare burdens by prioritizing urgent cases. It gives patient a quick health checks at doorstep before visiting doctor at outpatient department (OPD) of nearby hospital or connected to specialist doctor for tele-consultation to superspecialty clinic. Traditional symptom checkers has limitations such as low accuracy, inability to handle complex symptoms and lack of personalization due to static rule-based systems. This paper explores the development of an AI-enabled symptom checker with Python's Django framework integrating with machine learning (ML) models to enhance diagnostic precision and adaptability. The system uses ML algorithms like Logistic Regression, Support Vector Classifier (SVC) and KNearest Neighbors (KNN) to analyze symptoms and provide real-time personalized health assessments. The Django framework provide scalability, security and seamless integration with web-based interfaces for making it ideal for enhanced telemedicine services. Evaluation results demonstrate that the AI-driven symptom checker achieves high accuracy up to 100 % in some models. The study highlights the potential of AI in improving telemedicine by offering reliable preliminary diagnostics, enhancing accessibility and optimizing healthcare workflows.

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