ArticleJournal of advanced research2020
A decision support scheme for beta thalassemia and HbE carrier screening.
Article in Journal of advanced research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 25 citations in OpenAlex.
- Prediction ofFrontiers in medicine · 2026Article
- Multiclass classification of thalassemia types using complete blood count and HPLC data with machine learning.Scientific reports · 2025Article
- Development and validation of an interpretable risk prediction model for the early classification of thalassemia.NPJ digital medicine · 2025Article
- Interpretable machine learning models for beta thalassemia prediction: an explainable AI approach for smart healthcare 5.0.Frontiers in medicine · 2025Article
- A study to assess the occurrence of anaemia and beta-thalassemia in the tribal population residing in the Yadadri-Bhuvanagiri district of Telangana state.Journal of family medicine and primary care · 2024Article
- Multi-criteria decision making to validate performance of RBC-based formulae to screenBMC medical informatics and decision making · 2024Article
- Applications of Artificial Intelligence in Thalassemia: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2023Review
- Deep Learning Assisted Automated Assessment of Thalassaemia from Haemoglobin Electrophoresis Images.Diagnostics (Basel, Switzerland) · 2022Article
- Role of Red Cell Indices in Screening for Beta Thalassemia Trait: an Assessment of the Individual Indices and Application of Machine Learning Algorithm.Indian journal of hematology & blood transfusion : an official journal of Indian Society of Hematology and Blood Transfusion · 2021Article
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Authors and funding
10 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The most effective way to combat β-thalassemias is to prevent the birth of children with thalassemia major. Therefore, a cost-effective screening method is essential to identify β-thalassemia traits (BTT) and differentiate normal individuals from carriers. We considered five hematological parameters to formulate two separate scoring mechanisms, one for BTT detection, and another for joint determination of hemoglobin E (HbE) trait and BTT by employing decision trees, Naïve Bayes classifier, and Artificial neural network frameworks on data collected from the Postgraduate Institute of Medical Education and Research, Chandigarh, India. We validated both the scores on two different data sets and found 100% sensitivity of both the scores with their respective threshold values. The results revealed the specificity of the screening scores to be 79.25% and 91.74% for BTT and 58.62% and 78.03% for the joint score of HbE and BTT, respectively. A lower Youden's index was measured for the two scores compared to some existing indices. Therefore, the proposed scores can obviate a large portion of the population from expensive high-performance liquid chromatography (HPLC) analysis during the screening of BTT, and joint determination of BTT and HbE, respectively, thereby saving significant resources and cost currently being utilized for screening purpose.
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