Twin Support Vector Machines for Thalassemia Classification

Sa'id, Alva Andhika; Rustam, Zuherman; Novkaniza, Fevi; Setiawan, Qisthina Syifa; Maulidina, Faisa; Wibowo, Velery Virgina Putri

doi:10.1109/3ict53449.2021.9581956

Cited by 1 publication

(2 citation statements)

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“…Therefore, it is feasible that other researchers may make hypotheses utilizing this unique approach and make an effort to refute our findings in regions where they are more prevalent. Comparable studies, however, lacked a control group, and the bulk of studies [7,[46][47][48][49][50]59,63] similarly paid little attention to the group sizes we believed to be equal. As a consequence, we think that our study offers a more accurate evaluation.…”

Section: Discussionmentioning

confidence: 99%

“…Both the RELM and ELM algorithms produced an accuracy of 96.30% for female patients, 94.37% for male patients, and 95.59% when evaluating male and female patients simultaneously. TSVM [48] inspired by SVM is used to discover nonparallel hyperplanes to resolve a binary classification problem. Three commonly used kernels from earlier research are used to achieve this.…”

Section: Classifiers For Beta Thalassemiamentioning

confidence: 99%

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Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis

Saleem,

Aslam,

Lali

et al. 2023

Diagnostics

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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.

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