The purose of this repository is to develop and effective machine learning (ML) algorithm based off Cai John's Extreme Phenotype Sampling Machine Learning (ESL-ML) respository in order to predict clearance* for malaria when given dosages of pharmaceuticals. Additionally, this repository aims to assist clinicians, biologist, and other clinical or scientific field experts by providing a useful ML tool and to increase exposure to such methods.
The randomforest.py file analyzes the malaria data in order to predict clearence.
The ESL-ML_archieve contains the R files, which we base our analysis off of, comes from the ESL-ML repository.
The ESL-ML can be found here: https://github.com/caiwjohn/EPS-ML
The data used comes from ESP-ML and each dataset originates from Zhu et al. and Mok et al. respectfully. The datasets are included in the malaria_data directory.
In order to effectily run a random forest algorithm, the original malarai date: mok_meta.txt and zhu_meta.txt is converted into .csv files.
Python3
Scikit-Learn
python3 rf_mok.py
python3 rf_zhu.py
Clearence - measurement of per volume of plasma in which a substance is completely removed per unit time.