A Comparative Study on the Accuracy of Nine Combined Machine Learning Algorithms in Early Diagnosis of Tumors Based on High-dimensional dataFeng Li 1,*, Yue Xiaofei 2

Corresponding author: fengli, fengli@ouchn.edu.cn

DOI: 10.12201/bmr.202108.00016

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  Abstract: Nine combined classifiers composed of linear discriminant analysis (LDA), k-nearest neighbor method (KNN), decision tree (DT), support vector machine (SVM), artificial neural network (ANN), bagging, random forest method (RF), quadratic discriminant analysis (QDA) and logistic regression (LR) combined with dimension reduction method (partial least squares, PLS) were used to analyze the serum proteome data sets of young transgenic tumor mice and normal control mice to compare the accuracy of nine combined machine learning algorithms in early diagnosis of tumor based on high-dimensional data. The results showed that the classification accuracy of PLS-LR, PLS-LDA, PLS-ANN, PLS-SVM and PLS-QDA was higher.

  Key words: Machine; Learning, Early; Diagnosis, Tumors, High-dimensional; data

  Submit time: 9 October 2021

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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