Most ML enthusiasts don't know how to go byond developing ML models to help the targeted audience use them in their workflow. The objectives of this workshop are:
- ML developers understand where the model lives
- How the API exposes it
- How the frontend consumes it
- Expose web developers to ML workflows.
HCV dataset contains laboratory values of blood donors and Hepatitis C patients and demographic values like age and gender
Dataset Characteristics: Multivariate
Subject Area: Health and Medicine
Associated Tasks: Classification, Clustering
Feature Type:: Integer, Real
Instances: 615
Features: 12
What do the instances in this dataset represent?
Instances are patients
Additional Information
The target attribute for classification is Category (blood donors vs. Hepatitis C, including its progress: 'just' Hepatitis C, Fibrosis, Cirrhosis).
Has Missing Values?
Yes
Build a web app that allows lab technicians to input some lab tests to classify blood donors to either Blood Donors or Hepatitis C patients with the virus type. Lab tests are:
- Albumin Blood Test (ALB) g/L
- Alkaline Phosphatase Test (ALP) IU/L
- Alanine Transaminase Test (ALT) U/L
- Aspartate Transaminase Test (AST) U/L
- Bilirubin Blood Test (BIL) µmol/L
- Cholinesterase (CHE) kU/L
- Cholesterol (CHOL) mmol/L
- Creatinine Blod Test (CREA) µmol/L
- Gamma-Glutamyl Transpeptidase Test (GGT) U/L
- Protein Blood Test (PROT) g/L
- Frontend (React) → sends user input (lab results, age, gender)
- Backend (FastAPI) → forwards input to ML model, gets prediction
- ML Model (pickle/joblib artifact) → processes and returns classification
- Backend → sends prediction result back to frontend