Development of early prediction model for pregnancy-associated hypertension with graph-based semi-supervised learning

Implementation of graph-based semi-supervised learning

Requirements

This implementation is conducted by the following packages (to be installed independently)

• pandas
• numpy
• sklearn
• jupyter notebook: instructions of step-by-step process

Run graph-based semi-supervised learning

Similar implementation of graph-based SSL can be found at scikit-learn (sklearn.semi_supervised.LabelPropagation)
This file explains step by step how to use graph-based SSL.

Toy_Example_graph_based_SSL.ipynb # Instructions

Functional version and toy example demonstration

SSL.py # Function for graph-based SSL
test_main.py # Toy example

Note

1. Implementation of variable selection also can be found at scikit-learn
   a. from sklearn.linear_model import LogisticRegression
   b. from sklearn.ensemble import ExtraTreesClassifier
   c. from sklearn.svm import SVC
   d. from sklearn.feature_selection import RFE
   

2. To protect patient's private information, we can not share the data. (To reqeust data access, please contact authors: Seung Mi Lee. M.D., Ph.D.)