IMPLEMENTATION OF ePACK: AN INTELLIGENT AI-BASED HEALTH ASSISTANT FOR SYMPTOM CHECKING AND PRELIMINARY DIAGNOSTIC SUPPORT IN NIGERIA

Access to reliable healthcare information remains a significant challenge in many low-resource settings, particularly in developing countries where shortages of healthcare professionals and inadequate healthcare infrastructure hinder timely access to quality healthcare services. This study presents the design, implementation, and evaluation of ePACK, an intelligent Artificial Intelligence (AI)-based health assistant developed to support symptom checking and preliminary clinical decision support within the Nigerian healthcare context. The system enables users to describe symptoms in natural language, which are processed using Natural Language Processing (NLP) techniques and analysed through machine learning algorithms to identify potential health conditions. Clinical recommendations are further validated using a rule-based decision-support engine derived from the Practical Approach to Care Kit (PACK) Nigeria clinical guidelines.
The proposed framework integrates a responsive web-based user interface, NLP-driven symptom extraction, machine learning-based disease prediction, and a guideline-informed clinical recommendation engine. A Random Forest classifier was adopted as the primary prediction model due to its superior performance among the evaluated algorithms. The system was assessed using 200 patient cases obtained from healthcare facilities across Adamawa, Nasarawa, and Ondo States, Nigeria. Evaluation results demonstrated an overall diagnostic agreement rate of 78% when compared with physician-confirmed diagnoses, while user satisfaction assessments indicated positive perceptions regarding system usability, accessibility, and response efficiency.
The findings demonstrate the potential of AI-enabled digital health assistants to enhance healthcare accessibility, support patient triage, and provide preliminary health guidance in resource-constrained environments. Furthermore, the study highlights the value of integrating machine learning techniques with locally adapted clinical guidelines to improve the relevance, safety, and effectiveness of digital health interventions within the Nigerian healthcare system.