Published Paper Details:

This paper proposes a healthcare advisor chatbot that uses natural language processing (NLP) and machine learning (ML) algorithms to analyze user-input symptoms and predict the appropriate specialist for consultation. The framework uses the naive Bayes classifier, random forest classifier, and support vector machine (SVM) classifier to generate individual predictions based on the symptoms[1-3]. The final prediction is determined by the mode of these three predictions, ensuring a strong and accurate suggestion. The project aims to provide quick and efficient counsesling to patients, encouraging early treatment and preventing the worsening of diseases. By enabling users to interact naturally with the chatbot, effective communication is achieved. The framework is implemented using the Python programming language, combining NLP and ML techniques. The feasibility study confirms the technical, financial, and social feasibility of the project, leveraging established technologies, promoting cost-effective development and maintenance, and addressing the healthcare needs of users. By using different classifiers, the proposed chatbot improves the reliability and accuracy of specialist suggestions, leading to improved healthcare outcomes and patient satisfaction.