Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 230200172-6.doi: 10.11896/jsjkx.230200172

• Big Data & Data Science • Previous Articles     Next Articles

Disease Diagnosis Based on Projection Correlation and Random Forest Fusion Model

HAN Yimei1, LI Dongxi2   

  1. 1 College of Mathematics,Taiyuan University of Technology,Taiyuan 030024,China
    2 College of Big Data,Taiyuan University of Technology,Taiyuan 030024,China
  • Published:2023-11-09
  • About author:HAN Yimei,born in 1998,postgra-duate.Her main research interests include data mining and machine learning.
    LI Dongxi,born in 1982,Ph.D,associate professor.His main research interests include data analysis,data mining,machine learning,biostatistics and biomathematics.
  • Supported by:
    National Natural Science Foundation of China(11571009) and Research Project Supported by Shanxi Scholarship Council of China(2022-074).

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Abstract: The processing method for high-dimensional data has become one of the hot issues in the study of big data.In this paper,a two-stage random forest algorithm based on projection correlation is proposed,which integrates the projection correlation to measure the correlation of random variables with the random forest algorithm,and shows better results in prediction perfor-mance.Three kinds of gene data are used for experimental analysis.In the experiments on Leukemia and Colon datasets,the accuracy of the proposed model improves by 2.4%~6.5% compared with the existing algorithms.In the experiment on Breast data set,the accuracy rate of the proposed algorithm increases by 3.55%~9.26% compared with the traditional random forest model,and it also performs stably and well in various evaluation indexes of high-dimensional data of different scales.The application of the model in the field of disease diagnosis based on microarray data will provide more scientific and effective decision support for disease prevention,diagnosis and treatment.

Key words: Projection correlation, Random forest, High dimensional data, Feature selection, Machine learning

CLC Number: 

  • TP391
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