Risk analysis of Thalassemia using knowledge representation model: Diagnostic Bayesian Networks

Paokanta, Patcharaporn; Harnpornchai, Napat

doi:10.1109/bhi.2012.6211532

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…The indices using the average reticulocyte cell volume, the average reticulocyte hemoglobin content, % Micro-R, or mean platelet volume to separate β-thal from IDA were not analyzed as they are not routine in routine analysis in Croatia or North Macedonia ( 36 , 37 ). We encountered only four children with α-thalassemia trait, and therefore, we cannot support the usability of these indicies in α-thalassemia trait.…”

Section: Discussionmentioning

confidence: 99%

Differentinating between non-transfusion dependant β-thalassemia and iron deficinecy anemia in children using ROC and logistic regression analysis: two novel discrimination indices designed for pediatric patients

Turudic,

Vucak,

Kocheva

et al. 2024

Front. Pediatr.

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IntroductionThis cross-sectional study enrolled a group of 271 children with microcytic anemia in order to test the performance of 41 single and 2 composite formulas andindices in distinguishing between β-thalassemia (β-thal) and iron deficiency anemia (IDA) in the pediatric population.MethodsOptimal pediatric cut-off values from the previously published formulas and indices were generated using ROC analysis. Logistic regression in R using generalized linear models (GLM) generated two new indices.ResultsFormulas and indices with optimal cut-offvalues in children with accuracy ≥90% were (in descending order): Matos & Carvalho index, MDHL(Telmissani) formula, England and Fraser formula, Pornprasert index, Sirachainan index, Telmissani (MCHD) formula, CRUISE index, Hameed index, Sargolzaie formula and Zaghloul II index. The CroThalDD-LM1 index has an accuracy of 93.36% (AUC 0.986, 95% CI 0.975–0.997), while the second CroThalDD-LM2 index utilizes absolute reticulocyte count alongside CBC variables, with an accuracy of 96.77% (AUC 0.985, 95% CI 0.988–0.999).Discussion and conclusionWe recommend using aforementioned formulas and indices with corrected cut-off values and accuracy >90% alongside two new proposed indices. A comparison of both native and these new indices is encouraged. These are the first discrimination indices generated and designed precisely for the pediatric population, which includes preschool children.

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Section: Discussionmentioning

confidence: 99%

Differentinating between non-transfusion dependant β-thalassemia and iron deficinecy anemia in children using ROC and logistic regression analysis: two novel discrimination indices designed for pediatric patients

Turudic,

Vucak,

Kocheva

et al. 2024

Front. Pediatr.

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“…Genotype of β-Thalassemia patients was classified using the PCA method in [17]. Authors analysed all the basic components of the blood.…”

Section: Related Workmentioning

confidence: 99%

Classification of β-Thalassemia Carriers From Red Blood Cell Indices Using Ensemble Classifier

et al. 2021

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Thalassemia is viewed as a prevalent inherited blood disease that has gotten exorbitant consideration in the field of medical research around the world. Inherited diseases have a high risk that children will get these diseases from their parents. If both the parents are β-Thalassemia carriers then there are 25% chances that each child will have β-Thalassemia intermediate or β-Thalassemia major, which in most of its cases leads to death. Prenatal screening after counseling of couples is an effective way to control β-Thalassemia. Generally, identification of the Thalassemia carriers is performed by some quantifiable blood traits determined effectively by high-performance-liquid-chromatography (HPLC) test, which is costly, time-consuming, and requires specialized equipment. However, cost-effective and rapid screening techniques need to be devised for this problem. This study aims to detect β-Thalassemia carriers by evaluating red blood cell indices from the complete-blood-count test. The present study included Punjab Thalassemia Prevention Project Lab Reports dataset. The proposed SGR-VC is an ensemble of three machine learning algorithms: Support Vector Machine, Gradient Boosting Machine, and Random Forest. Comparative analysis proved that the proposed ensemble model using all indices of red blood cells is very effective in β-Thalassemia carrier screening with 93% accuracy.

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“…In [25], the authors compared the performance of several machine learning techniques, such as MLP, KNN, NB, Bayesian networks (BNs) and multinomial logistic regression (MLR) for screening β-thalassemia patients. Feature selection done by PCA and results showed the best algorithm was MLP with an accuracy of (86.6142%).…”

Section: Related Workmentioning

confidence: 99%

Hybrid Model for Prediction of Treatment Response in Beta-thalassemia Patients with Hepatitis C Infection

Hussein

Eldin

Abdo

et al. 2021

Digital Transformation Technology

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Patients with beta-thalassaemia major (β-TM) who get regular blood transfusions are at risk of iron overload and hepatitis C virus (HCV) infection. These double injuries together can lead to chronic liver damage. Treatment with pegylated interferon combined ribavirin (Peg-IFN/RBV) is associated with side effects that compromise the patients' quality of life. The efficacy of two anti-viral regimens (Peg-IFN/RBV) and Peg-IFN monotherapy were assessed using a machine learning model to identify patients who could achieve sustained virologic response (SVR) with HCV eradication. This paper is a follow-up study of our previous published paper that used a different method to address the same research question. A hybrid Neuro-SVM model was developed to improve the accuracy of classification that shows 98.83% in group 1 and 99.75 in group 2 and conveyed as a graphical user interface that can help the clinical support decision in the prediction of optimal treatment response. The model was compared to artificial neural network (ANN), support vector machine (SVM) and naïve Bayesian (NB). Using the hybrid model, it would be useful if we distinguish in advance those patients who may benefit from the approved direct anti-viral agents (DAAs) therapy from those who would not. Keywords Beta-thalassemia major • Synthetic minority oversampling • Machine learning • Artificial neural networks All authors assisted with manuscript preparation and revisions. AH and AA designed the study; SK provides the dataset with laboratory follow-up of enrolled patients. AH analyzed the data and wrote the manuscript. All authors read and approved the final version of the manuscript.

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Risk analysis of Thalassemia using knowledge representation model: Diagnostic Bayesian Networks

Cited by 7 publications

References 13 publications

Differentinating between non-transfusion dependant β-thalassemia and iron deficinecy anemia in children using ROC and logistic regression analysis: two novel discrimination indices designed for pediatric patients

Differentinating between non-transfusion dependant β-thalassemia and iron deficinecy anemia in children using ROC and logistic regression analysis: two novel discrimination indices designed for pediatric patients

Classification of β-Thalassemia Carriers From Red Blood Cell Indices Using Ensemble Classifier

Hybrid Model for Prediction of Treatment Response in Beta-thalassemia Patients with Hepatitis C Infection

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