VincentAlcazer/AIPAL
Artificial Intelligence-based Prediction of Acute Leukemia: a free and open-source software package built in R, with a user-friendly interface provided via Shiny, that enables clinical hematologists and biologists to diagnose the three main subtypes of acute leukemia based solely on 10 routine biological parameters.
This tool helps clinical hematologists and biologists quickly assess the likelihood of specific acute leukemia subtypes. By inputting 10 routine biological lab parameters, you receive a prediction for Acute Promyelocytic Leukemia (APL), Acute Lymphoblastic Leukemia (ALL), and Acute Myeloid Leukemia (AML). This supports clinical decision-making regarding potential leukemia diagnoses.
No commits in the last 6 months.
Use this if you are a clinical hematologist or biologist needing a rapid, AI-assisted prediction of acute leukemia subtypes based on standard lab tests.
Not ideal if you are looking for a definitive diagnosis or a tool that incorporates a broader range of complex diagnostic data beyond routine biological parameters.
Stars
10
Forks
1
Language
R
License
—
Category
Last pushed
Aug 16, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/VincentAlcazer/AIPAL"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
greenelab/pancancer
Building classifiers using cancer transcriptomes across 33 different cancer-types
udityamerit/Breast-Cancer-Prediction-using-different-ML-models
This project implements multiple machine learning algorithms to predict breast cancer diagnoses...
josegcpa/wbs-prediction
Code repository for project focusing on blood cell detection and diagnostic prediction from...
smb-h/breast-cancer-patterns-association-rule-mining
Breast Cancer Pattern Recognition through Association Rule Mining
Eije1/TS_Academy_Capstone_Project
This capstone project focuses on developing a machine learning pipeline to classify brain cancer...