A Mobile Application Front-End for Presenting Explainable AI Results in Diabetes Risk Estimation

TL;DR

This paper presents a mobile app front-end that visualizes explainable AI results for diabetes risk, making complex outputs understandable for users through visualizations and narratives, thereby improving user comprehension and engagement.

Contribution

The study introduces a native Android application that visualizes SHAP-based XAI outputs using bar and pie charts, combined with GPT-4o generated narratives, tailored for non-expert users in health contexts.

Findings

User comprehension improved with visualization and narratives (average score 4.31/5)

The app achieved 100% success in technical functionality testing

Participants felt more empowered to take preventive actions

Abstract

Diabetes is a significant and continuously rising health challenge in Indonesia. Although many artificial intelligence (AI)-based health applications have been developed for early detection, most function as "black boxes," lacking transparency in their predictions. Explainable AI (XAI) methods offer a solution, yet their technical outputs are often incomprehensible to non-expert users. This research aims to develop a mobile application front-end that presents XAI-driven diabetes risk analysis in an intuitive, understandable format. Development followed the waterfall methodology, comprising requirements analysis, interface design, implementation, and evaluation. Based on user preference surveys, the application adopts two primary visualization types - bar charts and pie charts - to convey the contribution of each risk factor. These are complemented by personalized textual narratives generated via integration with GPT-4o. The application was developed natively for Android using Kotlin and Jetpack Compose. The resulting prototype interprets SHAP (SHapley Additive exPlanations), a key XAI approach, into accessible graphical visualizations and narratives. Evaluation through user comprehension testing (Likert scale and interviews) and technical functionality testing confirmed the research objectives were met. The combination of visualization and textual narrative effectively enhanced user understanding (average score 4.31/5) and empowered preventive action, supported by a 100% technical testing success rate.