Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Machine Learning Driven Symptoms Diagnosis System
0
Zitationen
6
Autoren
2025
Jahr
Abstract
This paper presents an AI-driven system for symptom diagnosis that uniquely combines manual and conversational interfaces. Leveraging tree-based machine learning, our Random Forest model achieves 98.67% accuracy on 41 diseases—surpassing Ada Health (54%) and WebMD (42%). The implemented solution features: (1) a dual-interface design enabling both form-based input and Dialogflow chatbot interactions, (2) rigorous evaluation of Decision Tree vs. Random Forest models on 4,921 symptom-disease pairs, and (3) an open-source Flask implementation with encryption protocols aligned to HIPAA standards. The system demonstrates feasibility for preliminary diagnosis with explainable outputs, though clinical validation remains future work. Limitations include English-only support and 41-disease coverage, addressed through modular expansion capabilities.
Ähnliche Arbeiten
"Why Should I Trust You?"
2016 · 14.198 Zit.
A Comprehensive Survey on Graph Neural Networks
2020 · 8.576 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.084 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.444 Zit.
Artificial intelligence in healthcare: past, present and future
2017 · 4.382 Zit.