Predicting thalassemia using deep neural network based on red blood cell indices

Mo, Donghua; Zheng, Qian; Xiao, Bin; Li, Linhai

doi:10.1016/j.cca.2023.117329

Cited by 9 publications

(2 citation statements)

References 44 publications

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“…The technique is used to swiftly differentiate carriers of αT from those with low HbA2 levels. Thalassemia prediction is proposed by utilizing deep learning methods [58] utilizing genetic testing as the benchmark for performance. The thalassemia genetic test (2918) is the initial step.…”

Section: Classifiers For Alpha Thalassemiamentioning

confidence: 99%

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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Section: Classifiers For Alpha Thalassemiamentioning

confidence: 99%

Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis

Saleem,

Aslam,

Lali

et al. 2023

Diagnostics

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show abstract

“…It is particularly prevalent in regions such as South Africa, the Middle East, and Southeast Asia, as well as in low-and middle-income areas like coastal cities in southern China and rural areas in western China. China bears the highest burden of thalassemia globally, with approximately 30 million individuals affected by thalassemia-related mutations and 3 million suffering from moderate to severe forms, posing significant challenges to families and society (3)(4)(5). Due to the autosomal recessive inheritance pattern of thalassemia, parents who are asymptomatic can still have children affected by thalassemia.…”

Section: Introductionmentioning

confidence: 99%

Constructing a novel clinical indicator model to predict the occurrence of thalassemia in pregnancy through machine learning algorithm

Long,

Bai

2024

Front. Hematol.

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Thalassemia is one of the inherited hemoglobin disorders worldwide, resulting in ineffective erythropoiesis, chronic hemolytic anemia, compensatory hemopoietic expansion, hypercoagulability, etc., and when a mother carries the thalassemia gene, the child is more likely to have severe thalassemia. Furthermore, the economic and time costs of genetic testing for thalassemia prevent many thalassemia patients from being diagnosed in time. To solve this problem, we performed least absolute shrinkage and selection operator (LASSO) regression to analyze the correlation between thalassemia and blood routine indicators containing mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), and red blood cell (RBC). We then built a nomogram to predict the occurrence of thalassemia, and receiver operating characteristic (ROC) curve was used to verify the prediction efficiency of this model. In total, we obtained 7,621 cases, including 847 thalassemia patients and 6,774 non-thalassemia. Among the 847 thalassemia patients, with a positivity rate of 67.2%, 569 cases were positive for α-thalassemia, and with a rate of 31.5%, 267 cases were positive for β-thalassemia. The remaining 11 cases were positive for both α- and β-thalassemia. Based on machine learning algorithm, we screened four optimal indicators, namely, MCV, MCH, RBC, and MCHC. The AUC value of MCV, MCH, RBC, and MCHC were 0.907, 0.906, 0.796, and 0.795, respectively. Moreover, the AUC value of the prediction model was 0.911. In summary, a novel and effective machine learning model was built to predict thalassemia, which functioned accurately, and may provide new insights for the early screening of thalassemia in the future.

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Classification of beta-thalassemia major and HbE/beta-thalassemia via deep learning of image structure function image

Jarujareet,

Wiratchawa,

Petiwathayakorn

et al. 2025

Biomedical Signal Processing and Control

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Predicting thalassemia using deep neural network based on red blood cell indices

Cited by 9 publications

References 44 publications

Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis

Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis

Constructing a novel clinical indicator model to predict the occurrence of thalassemia in pregnancy through machine learning algorithm

Classification of beta-thalassemia major and HbE/beta-thalassemia via deep learning of image structure function image

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