AI health assistant combining transformers and XGBoost for multilingual care

Abstract

Limited healthcare access, shortages of healthcare professionals, and linguistic diversity continue to impede timely symptom assessment and healthcare delivery in low-resource settings such as Zimbabwe. Existing virtual health assistant (VHAs) are frequently cloud-dependent, English-centric, and lack interpretable decision-making, limiting their effectiveness in bandwidth-constrained and privacy-sensitive environments. This study proposes CIMAS HealthMate, a hybrid multilingual VHA that integrates transformer-based natural language processing (NLP) with an explainable extreme gradient boosting (XGBoost) decision model to provide accurate and transparent symptom triage. The framework employs the no language left behind (NLLB) model for offline English–Shona translation, bidirectional encoder representations from transformers (BERT)-based models for intent classification and medical entity recognition, and XGBoost for structured triage recommendation. The system was evaluated using a multilingual symptom corpus and an anonymized electronic health record-style dataset comprising approximately 23,000 patient records. Experimental results achieved translation accuracies of 76.5% for Shona-to-English and 82.2% for English-to-Shona, symptom extraction accuracy of 86.6%, and end-to-end triage accuracy of 93.3% with an F1-score of 93.3%. These findings demonstrate that the proposed hybrid architecture effectively combines multilingual language understanding, interpretable machine learning, and offline deployment to deliver reliable and privacy-preserving triage support. The proposed approach provides a scalable and practical solution for improving equitable digital healthcare services in multilingual, resource-constrained environments.