Open AccessSimple evolutionary algorithm for quantifying how medical history factors predict disease outcomes
Author(s) -
James P. Camp,
Hisham Al-Mubaid
Publication year - 2022
Publication title -
epic series in computing
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.21
H-Index - 7
ISSN - 2398-7340
DOI - 10.29007/7pd1
Subject(s) - logistic regression , decision tree , computer science , medical record , simple (philosophy) , artificial neural network , machine learning , artificial intelligence , health records , medical history , data mining , electronic medical record , health care , medicine , epistemology , economics , economic growth , internet privacy , philosophy
The medical history information contained in electronic health records (EHR) is a valuable and largely untapped data mining source for predicting patient outcomes and thereby improving treatment. This paper presents a simple but novel evolutionary algorithm (EA) for identifying how various medical history and demographic factors predict clinical outcomes. For this initial study, our EA was tested using synthetic data concerning COVID-19 hospitalization rates and we show that the EA results are more informative than logistic regression, neural network, or decision tree results.