Abstract:

Infectious diseases remain a serious global health concern, making early detection and prevention essential for controlling their spread. This study introduces an AI-powered medical chatbot designed to assist in predicting infectious diseases based on user-provided symptoms and medical history. By utilizing machine learning and natural language processing (NLP), the chatbot engages users in a conversational manner, processes their input, and matches it against a comprehensive dataset of infectious disease patterns. This enables the system to provide an assessment of the likelihood of a specific disease, offering timely insights that can aid in early intervention. The chatbot is developed with accessibility in mind, featuring a user-friendly interface that operates on both web and mobile platforms. To enhance prediction accuracy, the system incorporates real-time epidemiological data, accounting for ongoing disease outbreaks and regional health trends. Built using a supervised learning approach, the chatbot is trained on a diverse dataset consisting of past medical records, infectious disease databases, and user interactions. Performance evaluations indicate that the model achieves high accuracy in predicting a variety of common infectious diseases, showcasing its potential as a reliable tool for healthcare support. Beyond prediction, the chatbot offers personalized medical advice and recommends preventive measures or further diagnostic steps, making it particularly valuable in underprivileged areas with limited access to healthcare professionals. Additionally, it serves as a decision-support tool for healthcare providers, assisting them in patient triage and streamlining the diagnostic process. The study highlights the broader implications of this technology for public health, especially in outbreak management and resource-limited settings, while also discussing future improvements to enhance the chatbot’s predictive capabilities.

Description:

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