# BioLearner
##### *BioLearner: A Machine Learning-Powered Smart Heart Disease Risk Prediction System Utilizing Biomedical Markers*

# Code Description 
The file Source_Code.ipynb is a Jupyter Notebook. The program is written in Python 
and uses libraries from Scikit Learn, Keras, TensorFlow, and Numpy.

The code contains some algorithms that are commented out as they take a VERY long 
time to run, but can be uncommented to run anyway. The algorithms can be tweaked 
and altered to give better results, this is specified in the code (in comments).

The dataset used for this study is of a sensitive medical nature and thus cannot
be shared. It is the UCI (University of California Irvine) Heart Disease dataset
from the Cleveland Clinic.

Without this dataset, the code will not run, but the dataset can be requested from
aforementioned sources.

### Warning
The copyright of the shared work is reserved. Reference should be cite to the BioLearner article for use in academic studies.

### To cite (text)
Syed Saad Amer, Gurleen Wander, Manmeet Singh, Rami Bahsoon, Nicholas R. Jennings and Sukhpal Singh Gill, "BioLearner: A Machine Learning-Powered Smart Heart Disease Risk Prediction System Utilizing Biomedical Markers." Journal of Interconnection Networks (2021): 2145003. Publication Link: https://www.worldscientific.com/doi/abs/10.1142/S0219265921450031

### To cite (.bib)
@article{amer2021biolearner, title={BioLearner: A Machine Learning-Powered Smart Heart Disease Risk Prediction System Utilizing Biomedical Markers}, author={Amer, Syed Saad and Wander, Gurleen and Singh, Manmeet and Bahsoon, Rami and Jennings, Nicholas R and Gill, Sukhpal Singh}, journal={Journal of Interconnection Networks}, pages={2145003}, year={2021}, publisher={World Scientific}