Symptom-Based Early UTI Screening Tool for Rural Healthcare

Added Nov 2025 3 design docs

Urinary tract infections are among the most common infections affecting women worldwide, yet in rural and underserved communities they often go unnoticed until they escalate into painful, costly complications. Seeing a clinician for an early assessment is not always practical, so a lightweight screening tool that works from symptom descriptions and basic health data can make a real difference in prompting timely care. The intern builds a complete screening application around this need. A questionnaire-style interface built with streamlit collects symptoms such as burning sensation, urinary frequency, and fever alongside simple health indicators, and a machine-learning classifier written in python estimates the likelihood of a UTI from those inputs. A fastapi backend exposes the trained model as an inference service, validates incoming data, and keeps the interface cleanly separated from the prediction logic. The app also surfaces educational content on symptoms, prevention, and red flags that warrant an urgent doctor visit. Completing the project takes the intern through the full arc of applied machine learning in healthcare: preparing training data, evaluating a classifier honestly, serving it behind an API, and presenting predictions responsibly as guidance rather than diagnosis.

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