Open AccessA Rough Set and Cellular Genetic Fusion Algorithm for Acute Critical Disease Prediction
Author(s) -
Hongxin Wang,
Lijing Jia,
Zhuang Hu,
Xueyan Li,
Yuzhuo Zhao,
Shuxiao Pan,
Kainan Wu,
Jing Li,
Tanshi Li
Publication year - 2020
Publication title -
international journal of computers, communications and control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.422
H-Index - 33
eISSN - 1841-9844
pISSN - 1841-9836
DOI - 10.15837/ijccc.2020.6.3894
Subject(s) - adaboost , support vector machine , computer science , naive bayes classifier , logistic regression , genetic algorithm , machine learning , artificial intelligence , algorithm , bayes' theorem , data mining , rough set , bayesian probability
This study is to solve the problems of an overly-broad scale of medical indicators, lack of retrospective research samples, insufficient depth of data mining, and low disease prediction accuracy. In this paper, we propose an intelligent screening algorithm that combines a genetic algorithm, cellular automata, and rough set theory. This algorithm can achieve high accuracy in predicting patient outcomes with a small number of indicators. And we compare it with the traditional genetic algorithm. We built the prediction model with 64 indicators based on the logistic regression (AUC 0.8628), support vector machine (AUC 0.5319), Naïve Bayes (AUC 0.7102), and AdaBoost algorithms (AUC 0.9095). Using the cellular genetic algorithm for attribute screening not only effectively reduces the number of indicators but also achieve almost the same accuracy of prediction with 8 indicators based on the logistic regression (AUC 0.8782), support vector machine (AUC 0.8525), Naïve Bayes (AUC 0.8408), and AdaBoost algorithms (AUC 0.8770). Compared with the traditional scoring system, the predictive model established in this paper can more accurately predict rebleeding accidents based on physiological test indicators and continuous patient indicators.