用可视化+AI生成文本，让糖尿病风险预测更透明易懂
A Mobile Application Front-End for Presenting Explainable AI Results in Diabetes Risk Estimation
- 用柱状图和饼图展示各风险因子贡献，结合GPT-4o生成个性化解释文案
- 用户理解度平均得分4.31/5，技术测试通过率100%
- 适合医疗科普、慢病管理场景，帮助非专业人士看懂AI诊断
糖尿病是印度尼西亚持续上升的重大健康挑战。尽管已有许多基于人工智能的健康应用用于早期筛查，但多数为“黑箱”模型，缺乏预测透明性。可解释人工智能（XAI）方法可解决此问题，但其技术输出常难以被非专家理解。本研究旨在开发一款移动端前端应用，以直观、易懂的方式呈现基于XAI的糖尿病风险分析。开发采用瀑布模型，包括需求分析、界面设计、实现与评估。根据用户偏好调研，应用采用柱状图与饼图展示各风险因子贡献，并集成GPT-4o生成个性化文本叙述。应用以Kotlin和Jetpack Compose原生开发于Android平台。原型将SHAP（SHapley Additive exPlanations）这一关键XAI方法转化为易懂的图形化展示与文本描述。通过用户理解度测试（李克特量表与访谈）和技术功能测试，验证了研究目标达成。可视化与文本叙述结合显著提升用户理解度（平均分4.31/5），并推动预防行为，技术测试成功率达100%。
原文摘要 · Abstract (English)
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.
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