Haemoglobin Levels Analysis Using Robust Partial Least Square Regression Models

Ali, Taha H; Mahmoud Bazid, Mahammad

doi:10.33899/csmj.2025.156730.1163

	Haemoglobin Levels Analysis Using Robust Partial Least Square Regression Models
AL-Rafidain Journal of Computer Sciences and Mathematics
Article 10, Volume 19, Issue 1, June 2025, Pages 84-95 PDF (747.84 K)
Document Type: Research Paper
DOI: 10.33899/csmj.2025.156730.1163
Authors
Taha H Ali^* ¹; Mahammad Mahmoud Bazid²
¹prof.
²Salahaddin University
Abstract
The Robust Partial Least Square Regression method is used to handle outliers and increase the explanation proportion, but it does not reduce the average of the mean square error. In this article, three methods are proposed to handle the problem of outliers, reduce the average of the mean square error, and increase the explanation proportion of the predictor and dependent variables. The first proposed method (Iteration) depends on identifying outliers by estimating the initial Partial Least Square Regression and then estimating outliers based on the residuals of those values to obtain the lowest mean square error, while the second and third proposed methods depend on a hybrid process between iteration and robust Partial Least Square Regression. The proposed and conventional methods were applied to estimate PLSR models on data Datasets for various ordinary patients in Iraq. The Dataset provides the patients’ Cell Blood Count test information that can be used to create a Hematology diagnosis/prediction system. Also, this Data was collected in 2022 from Al-Zahraa Al-Ahly Hospital. The proposed iterative method with higher efficiency provided 5 variables' importance in the projection score that explain the changes in HGB levels.
Keywords
Haemoglobin Levels; Partial Least Squares Regression; Robust Partial Least Squares Regression; Outliers; noise data

Statistics Article View: 103 PDF Download: 42