A Rough Set and Cellular Genetic Fusion Algorithm for Acute Critical Disease Prediction

Authors

  • Hongxin Wang
  • Lijing Jia
  • Heng Zhuang
  • Xueyan Li
  • Yuzhuo Zhao
  • Shuxiao Pan
  • Kainan Wu
  • Jing Li Department of School of Economics and Management, Beijing Jiaotong University
  • Tanshi Li

Keywords:

cellular genetic algorithm, key indicator, disease prediction, machine learning

Abstract

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), Naí¯ve 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), Naí¯ve 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.

Author Biographies

Hongxin Wang

Department of Emergency,
Armed Police Characteristic Medical Center, Tianjin, China

Lijing Jia

Department of Emergency,
Chinese PLA General Hospital, Beijing, China

Heng Zhuang

Department of Emergency,
Chinese PLA General Hospital, Beijing, China

Xueyan Li

Management School
Beijing Union University, Beijing, China

Yuzhuo Zhao

Department of Emergency,
Chinese PLA General Hospital, Beijing, China

Shuxiao Pan

Department of School of Economics and Management
Beijing Jiaotong University, Beijing, China

Kainan Wu

Department of School of Economics and Management
University of Chinese Academy of Sciences, Beijing, China

Tanshi Li

Department of Emergency,
Chinese PLA General Hospital, Beijing, China

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2020-11-20

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