Regional Language Framework for Healthcare: A Kannada Medical Chatbot Approach for Rural Areas

Abstract

The Natural language processing (NLP) healthcare chatbot support system in rural settings is designed to support the healthcare needs of rural communities by harnessing the power of efficient technologies to make available and convenient healthcare services. In rural areas, access to medical facilities and services is often insufficient, resulting in delayed diagnosis and treatment. The presented Kannada Medical Chatbot (KMC) in rural settings represents a new approach to the problem of ensuring a smooth flow of information between the rural population and medical services, in particular, in regional languages such as Kannada, through the use of NLP methods integrated into a chatbot application. The proposed KMC that will deliver the rural population with the assistance of the NLP system is based on the opportunities of natural language comprehension and generation to analyse the query of a user and provide helpful medical materials with personal assistance. It also incorporates a detailed medical knowledge base to provide accurate answers and enable the chatbot to assist with symptom identification, preventive healthcare information, and general medical advice. The system will also be able to walk individuals through proactive self-diagnostic procedures, which will help with remote healthcare management. To make the chatbot more effective, the system combines Machine Learning algorithms that improve with every interaction of the user, and further, as time passes, the system becomes more precise and pertinent. Privacy and security for confidential health information follow the priority to protect sensitive data and comply with regulatory levels, and to win the trust of users. The proposed KMC framework of supporting healthcare in rural communities not only supports current healthcare needs but also acts as a learning resource, promoting health literacy in the population. The KMC chatbot reached 90 % user engagement, 95% information accuracy and 92% user satisfaction, which shows an average performance increase of more than 30% relative to the baseline systems.

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