TY - JOUR
T1 - A decision support scheme for beta thalassemia and HbE carrier screening
AU - Das, Reena
AU - Datta, Saikat
AU - Kaviraj, Anilava
AU - Sanyal, Soumendra Nath
AU - Nielsen, Peter
AU - Nielsen, Izabela
AU - Sharma, Prashant
AU - Sanyal, Tanmay
AU - Dey, Kartick
AU - Saha, Subrata
N1 - © 2020 THE AUTHORS. Published by Elsevier BV on behalf of Cairo University.
PY - 2020/7
Y1 - 2020/7
N2 - 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.
AB - 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.
KW - Artificial neural networks
KW - Decision trees
KW - Thalassemia carrier screening
UR - http://www.scopus.com/inward/record.url?scp=85083658914&partnerID=8YFLogxK
U2 - 10.1016/j.jare.2020.04.005
DO - 10.1016/j.jare.2020.04.005
M3 - Journal article
C2 - 32368356
AN - SCOPUS:85083658914
VL - 24
SP - 183
EP - 190
JO - Journal of Advanced Research
JF - Journal of Advanced Research
SN - 2090-1232
ER -