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Chabriel, G., Kleinsteuber, M., Moreau, E., et al. (2014) Joint Matrices Decompositions and Blind Source Separation: A Survey of Methods, Identification, and Applications. IEEE Signal Processing Magazine, 31, 34-43.
https://doi.org/10.1109/MSP.2014.2298045

has been cited by the following article:

• TITLE: An Application of Machine Learning to Thalassemia Diagnosis

  AUTHORS: Sitan Liu

  KEYWORDS: Multicollinearity, Statistical Analysis Models, Data Mining, PCA-LR, PLS

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.2, February 29, 2024

  ABSTRACT: Mediterranean anemia is a genetic disease that currently relies heavily on expert clinical experience to determine whether patients are affected. This method is overly reliant on expert experience and is not precise enough. This paper proposes two modeling methods to predict whether patients have Mediterranean anemia. The first method involves using Principal Component Analysis (PCA) to reduce the dimensionality of the data, followed by logistic regression modeling (PCA-LR) on the reduced dataset. The second method involves building a Partial Least Squares Regression (PLS) model. Experimental results show that the prediction accuracy of the PCA-LR model is 87.5% (degree = 2, λ=4), and the prediction accuracy of the PLS model is 92.5% (ncomp = 4), indicating good predictive performance of the models.