Medical ChatBot Assistance for Primary Clinical Guidance using Machine Learning Techniques

Abstract

Hospitals serve as critical hubs for global healthcare, offering essential medical services. However, a pressing issue is the lack of 24/7 medical facilities in rural areas, particularly during the overnight hours. To bridge this gap, we propose the Robotic Medical Support ChatBot (RMSCB) system. This innovative online platform leverages machine learning to autonomously predict medical diagnoses and provide temporary solutions, accessible at any time. The RMSCB system comprises three key stages: pre-processing, training, and classification. In the pre-processing stage, relevant keywords are extracted using the Pre-Fixed Stopping Words Model (PFSWM). The training stage categorizes pre-processed questions into classes based on the Pre-Fixed Class Label Model (PFCLM), associating medical solutions via the Pre-Fixed Answer and Question Model (PFAQM). In the classification stage, the RMSCB processes clinical questions from patients via mobile or personal systems, mapping and identifying solutions based on probability. Experimental results demonstrate the efficacy of the RMSCB system, offering timely first aid medication information and alleviating the need for immediate physician consultation. This technological solution addresses critical healthcare gaps, especially in regions with limited access to continuous medical services.