DEVELOPMENT OF A TEXT-BASED, AMHARIC-LANGUAGE CHATBOT FOR MATERNAL HEALTH CONSULTATION USING SUPERVISED MACHINE LEARNING

Abstract

Maternal health continues to be a critical concern in Ethiopia, where language barriers and limited access to healthcare information contribute to high rates of preventable pregnancy complications. Motivated by the need to improve maternal outcomes through accessible and culturally appropriate solutions, this study introduces an Amharic-based pregnancy chatbot. The chatbot is designed to provide expecting mothers with personalized, trustworthy, and timely maternal health guidance throughout their pregnancy journey. Using natural language processing (NLP), the chatbot interacts with users in Amharic, addressing common concerns and delivering information on prenatal care, nutrition, warning signs, emotional well-being, childbirth, and postpartum care. The methodology involves integrating the chatbot with local health resources and deploying it via mobile platforms to ensure 24/7 conversational support. The developed chatbot achieved approximately 100% training accuracy and 75% test accuracy in intent classification using an ensemble model averaging approach. Beyond technical validation, this study establishes a comprehensive theoretical framework grounded in the Technology Acceptance Model (TAM) and Nielsen's Usability Heuristics to evaluate usability, acceptance, and user satisfaction. This framework addresses the critical gap between technical functionality and real-world adoption, providing a methodological foundation for future empirical validation with target users in Ethiopia's maternal healthcare context.