Haemoglobin Levels Analysis Using Robust Partial Least Square Regression Models
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 erro...
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| Format: | Article |
| Language: | English |
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Mosul University
2025-06-01
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| Series: | Al-Rafidain Journal of Computer Sciences and Mathematics |
| Subjects: | |
| Online Access: | https://csmj.uomosul.edu.iq/article_186834_3e85ed0b3f50d04eeeaa43a51e14d29e.pdf |
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| author | Taha Ali Mahammad Mahmoud Bazid |
| author_facet | Taha Ali Mahammad Mahmoud Bazid |
| author_sort | Taha Ali |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ed68882e3dda471abf2181eccf4b7ac4 |
| institution | Kabale University |
| issn | 1815-4816 2311-7990 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Mosul University |
| record_format | Article |
| series | Al-Rafidain Journal of Computer Sciences and Mathematics |
| spelling | doaj-art-ed68882e3dda471abf2181eccf4b7ac42025-08-20T04:00:44ZengMosul UniversityAl-Rafidain Journal of Computer Sciences and Mathematics1815-48162311-79902025-06-01191849510.33899/csmj.2025.156730.1163186834Haemoglobin Levels Analysis Using Robust Partial Least Square Regression ModelsTaha Ali0Mahammad Mahmoud Bazid1prof.Salahaddin UniversityThe 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.https://csmj.uomosul.edu.iq/article_186834_3e85ed0b3f50d04eeeaa43a51e14d29e.pdfhaemoglobin levelspartial least squares regressionrobust partial least squares regressionoutliersnoise data |
| spellingShingle | Taha Ali Mahammad Mahmoud Bazid Haemoglobin Levels Analysis Using Robust Partial Least Square Regression Models Al-Rafidain Journal of Computer Sciences and Mathematics haemoglobin levels partial least squares regression robust partial least squares regression outliers noise data |
| title | Haemoglobin Levels Analysis Using Robust Partial Least Square Regression Models |
| title_full | Haemoglobin Levels Analysis Using Robust Partial Least Square Regression Models |
| title_fullStr | Haemoglobin Levels Analysis Using Robust Partial Least Square Regression Models |
| title_full_unstemmed | Haemoglobin Levels Analysis Using Robust Partial Least Square Regression Models |
| title_short | Haemoglobin Levels Analysis Using Robust Partial Least Square Regression Models |
| title_sort | haemoglobin levels analysis using robust partial least square regression models |
| topic | haemoglobin levels partial least squares regression robust partial least squares regression outliers noise data |
| url | https://csmj.uomosul.edu.iq/article_186834_3e85ed0b3f50d04eeeaa43a51e14d29e.pdf |
| work_keys_str_mv | AT tahaali haemoglobinlevelsanalysisusingrobustpartialleastsquareregressionmodels AT mahammadmahmoudbazid haemoglobinlevelsanalysisusingrobustpartialleastsquareregressionmodels |