Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
SymptoBuddy: A Progressive Web-Based AI Symptom Checker to Enhance Preliminary Health Assessment
0
Zitationen
4
Autoren
2025
Jahr
Abstract
This paper presents SymptoBuddy, a Progressive Web Application (PW A) designed to support preliminary health assessment. The system integrates a trained Artificial Neural Network (ANN) built with TensorFlow to predict common illnesses from user-selected symptoms. Unlike text-based systems, SymptoBuddy uses a predefined checklist of symptoms, which reduces input errors and improves usability. The model was trained on a curated Kaggle dataset and achieved an accuracy of 88%. Users receive instant feedback that includes the predicted illness, an overview of the condition, its common symptoms, possible causes, and suggested next steps. With its offline capability and privacy-focused design, SymptoBuddy has the potential to improve self-assessment and reduce unnecessary healthcare visits, particularly in low-resource settings.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.316 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.177 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.575 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.468 Zit.