Design and Implementation of a Web Application for Early Detection of Dementia - A Comparative Analysis with Native Applications

This study addresses the need for accessible, maintainable tools for Early Diagnosis of Dementia by designing and implementing a Web Application and comparing its performance with Native Applications. The application’s backend is built with Python (Flask), while the frontend uses HTML, CSS, and JavaScript. Key features include a self-diagnostic test based on the SDQ Questionnaire, a Counseling Center Locator powered by a JSON dataset, and Hospital Search functionality integrated via the Kakao Maps API. Performance evaluations consider Server Response Time, Ease of Deployment, Maintenance Effort, and Cross-Platform Compatibility. Results demonstrate that the Web Application offers superior maintainability and broader accessibility across devices, whereas Native Applications outperform in resource-intensive operations, delivering faster Local Rendering and more seamless User Experience. These findings suggest that a hybrid approach—leveraging the strengths of both paradigms—may optimize dementia screening tools for diverse user needs and technological constraints.