Role of Red Cell Indices in Screening for Beta Thalassemia Trait: an Assessment of the Individual Indices and Application of Machine Learning Algorithm

Jahan, Aarzoo; Singh, Garima; Gupta, Ruchika; Sarin, Namrata; Singh, Sompal

doi:10.1007/s12288-020-01373-x

Cited by 7 publications

(8 citation statements)

References 17 publications

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“…Other studies have shown that there are tools used in the peripheral setting for screening thalassemia patients that are associated with quite specific and sensitive applications. (Jahan et al, 2021). The application of a website-based expert system for the management of thalassemia patients in order to provide treatment recommendations and support for lifelong care of thalassemia patients can help patients in real life as an effort to improve the management of thalassemia patients (Banjar et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Mobile App Deployment for Quality-of-Life Patient with Thalassemia: A narrative review

Ayu

Mulyono²

2023

bik

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Thalassemia is a hereditary hemolytic anemia disease that is inherited from parents. Occurs due to the inability of red blood cells to form hemoglobin. Thus causing thalassemia patients to undergo transfusion therapy for life. The mobile application contains detailed information about thalassemia that can be easily accessed by thalassemia patients and their families aiming to maintain the quality of life of patients with thalassemia. The purpose of this literature review is to determine the effectiveness of mobile applications applied to the quality of life of patients thalassemia. The method used in this preparation is by electronic search of online databases, namely: Google Scholar Science Direct, PROQUEST. The criteria for writing are full text and publications in 2018-2022. Research search found 10 articles had been extracted. The results obtained are mobile applications for the quality of life of patients with thalassemia have a positive response and dynamically need to be developed.

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Section: Discussionmentioning

confidence: 99%

Mobile App Deployment for Quality-of-Life Patient with Thalassemia: A narrative review

Ayu

Mulyono²

2023

bik

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“…Jahan et al [26] investigated the research on red cell indices utilizing machine learning techniques, such as an artifcial neural network (ANN), to detect beta-thalassemia traits (BTT) in pregnant women. Te optimal cutof for each index and the BTT detection test characteristics was determined using a receiver operating characteristic (ROC) curve analysis.…”

Section: Related Workmentioning

confidence: 99%

“…Nave Bayes is a superfcial learning algorithm that uses the Bayes rule and assumes attributes are class-dependent. Due to its processing efciency and other benefts, nave Bayes is commonly used in practice [26].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-Based Fusion Model for β-Thalassemia Carriers Prediction Using Machine Learning Technique

Ibrahim,

Abbas,

Fatima

et al. 2024

Advances in Fuzzy Systems

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The abnormality of haemoglobin in the human body is the fundamental cause of thalassemia disease. Thalassemia is considered a common genetic blood condition that has received extensive investigation in medical research globally. Likely, inherited disorders will be passed down to children from their parents. If both parents are beta Thalassemia carriers, 25% of their children will have intermediate or major beta thalassemia, which is fatal. An efficient method of beta thalassemia is prenatal screening after couples have received counselling. Identifying Thalassemia carriers involves a costly, time-consuming, and specialized test using quantifiable blood features. However, cost-effective and speedy screening methods must be developed to address this issue. The demise rate due to thalassemia development is outstandingly high around the globe. The passing rate due to thalassemia development can be reduced by following the proper procedure early; otherwise, it significantly impacts the body. A machine learning-based late fusion model proposes the detection of beta-thalassemia carriers by analyzing red blood cells. This study applied the late fusion technique to employ four machine learning algorithms. For identifying the beta-thalassemia carriers, logistics regression, Naïve Bayes, decision tree, and neural network have achieved an accuracy of 94.01%, 93.15%, 97.93%, and 98.07%, respectively, by using the features-based dataset. The late fusion-based ML model achieved an overall accuracy of 96% for detecting beta-thalassemia carriers. The proposed late fusion model performs better than previously published approaches regarding efficiency, reliability, and precision.

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“…In India, βT diagnosis of expectant mothers is carried out utilizing three classifiers. To eliminate bias, use NB, C4.5 DT, and a back-propagation ANN [44] implementation in R Studio on a balanced number of selected βT and non-BTT individuals. C4.5 DT outperforms with an accuracy of 88.56% as opposed to ANN's accuracy of 85.95% and NB's accuracy of 82.49%.…”

Section: Classifiers For Beta 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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Role of Red Cell Indices in Screening for Beta Thalassemia Trait: an Assessment of the Individual Indices and Application of Machine Learning Algorithm

Cited by 7 publications

References 17 publications

Mobile App Deployment for Quality-of-Life Patient with Thalassemia: A narrative review

Mobile App Deployment for Quality-of-Life Patient with Thalassemia: A narrative review

Fuzzy-Based Fusion Model for β-Thalassemia Carriers Prediction Using Machine Learning Technique

Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis

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