Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis
Thalassemia represents one of the most common genetic disorders worldwide, characterized by defects in hemoglobin synthesis. The affected individuals suffer from malfunctioning of one or more of the four globin genes, leading to chronic hemolytic anemia, an imbalance in the hemoglobin chain ratio, i...
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| Format: | Article |
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MDPI AG
2023-11-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/22/3441 |
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| author | Muniba Saleem Waqar Aslam Muhammad Ikram Ullah Lali Hafiz Tayyab Rauf Emad Abouel Nasr |
| author_facet | Muniba Saleem Waqar Aslam Muhammad Ikram Ullah Lali Hafiz Tayyab Rauf Emad Abouel Nasr |
| author_sort | Muniba Saleem |
| collection | DOAJ |
| description | Thalassemia represents one of the most common genetic disorders worldwide, characterized by defects in hemoglobin synthesis. The affected individuals suffer from malfunctioning of one or more of the four globin genes, leading to chronic hemolytic anemia, an imbalance in the hemoglobin chain ratio, iron overload, and ineffective erythropoiesis. Despite the challenges posed by this condition, recent years have witnessed significant advancements in diagnosis, therapy, and transfusion support, significantly improving the prognosis for thalassemia patients. This research empirically evaluates the efficacy of models constructed using classification methods and explores the effectiveness of relevant features that are derived using various machine-learning techniques. Five feature selection approaches, namely Chi-Square (χ2), Exploratory Factor Score (EFS), tree-based Recursive Feature Elimination (RFE), gradient-based RFE, and Linear Regression Coefficient, were employed to determine the optimal feature set. Nine classifiers, namely K-Nearest Neighbors (KNN), Decision Trees (DT), Gradient Boosting Classifier (GBC), Linear Regression (LR), AdaBoost, Extreme Gradient Boosting (XGB), Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Support Vector Machine (SVM), were utilized to evaluate the performance. The χ2 method achieved accuracy, registering 91.56% precision, 91.04% recall, and 92.65% f-score when aligned with the LR classifier. Moreover, the results underscore that amalgamating over-sampling with Synthetic Minority Over-sampling Technique (SMOTE), RFE, and 10-fold cross-validation markedly elevates the detection accuracy for αT patients. Notably, the Gradient Boosting Classifier (GBC) achieves 93.46% accuracy, 93.89% recall, and 92.72% F1 score. |
| first_indexed | 2024-03-09T16:53:59Z |
| format | Article |
| id | doaj.art-962dd384b5de48ecb7355cce978323f9 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T16:53:59Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-962dd384b5de48ecb7355cce978323f92023-11-24T14:37:36ZengMDPI AGDiagnostics2075-44182023-11-011322344110.3390/diagnostics13223441Predicting Thalassemia Using Feature Selection Techniques: A Comparative AnalysisMuniba Saleem0Waqar Aslam1Muhammad Ikram Ullah Lali2Hafiz Tayyab Rauf3Emad Abouel Nasr4Department of Computer Science & Information Technology, The Government Sadiq College Women University Bahawalpur, Bahawalpur 63100, PakistanDepartment of Information Security, The Islamia University of Bahawalpur, Bahawalpur 63100, PakistanDepartment of Information Sciences, University of Education Lahore, Lahore 54770, PakistanCentre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent ST4 2DE, UKIndustrial Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi ArabiaThalassemia represents one of the most common genetic disorders worldwide, characterized by defects in hemoglobin synthesis. The affected individuals suffer from malfunctioning of one or more of the four globin genes, leading to chronic hemolytic anemia, an imbalance in the hemoglobin chain ratio, iron overload, and ineffective erythropoiesis. Despite the challenges posed by this condition, recent years have witnessed significant advancements in diagnosis, therapy, and transfusion support, significantly improving the prognosis for thalassemia patients. This research empirically evaluates the efficacy of models constructed using classification methods and explores the effectiveness of relevant features that are derived using various machine-learning techniques. Five feature selection approaches, namely Chi-Square (χ2), Exploratory Factor Score (EFS), tree-based Recursive Feature Elimination (RFE), gradient-based RFE, and Linear Regression Coefficient, were employed to determine the optimal feature set. Nine classifiers, namely K-Nearest Neighbors (KNN), Decision Trees (DT), Gradient Boosting Classifier (GBC), Linear Regression (LR), AdaBoost, Extreme Gradient Boosting (XGB), Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Support Vector Machine (SVM), were utilized to evaluate the performance. The χ2 method achieved accuracy, registering 91.56% precision, 91.04% recall, and 92.65% f-score when aligned with the LR classifier. Moreover, the results underscore that amalgamating over-sampling with Synthetic Minority Over-sampling Technique (SMOTE), RFE, and 10-fold cross-validation markedly elevates the detection accuracy for αT patients. Notably, the Gradient Boosting Classifier (GBC) achieves 93.46% accuracy, 93.89% recall, and 92.72% F1 score.https://www.mdpi.com/2075-4418/13/22/3441thalassemiaclassificationfeature selectionfilter-basedwrapper and embedded method |
| spellingShingle | Muniba Saleem Waqar Aslam Muhammad Ikram Ullah Lali Hafiz Tayyab Rauf Emad Abouel Nasr Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis Diagnostics thalassemia classification feature selection filter-based wrapper and embedded method |
| title | Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis |
| title_full | Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis |
| title_fullStr | Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis |
| title_full_unstemmed | Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis |
| title_short | Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis |
| title_sort | predicting thalassemia using feature selection techniques a comparative analysis |
| topic | thalassemia classification feature selection filter-based wrapper and embedded method |
| url | https://www.mdpi.com/2075-4418/13/22/3441 |
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