Browsing by Author "Premawardhena, A. P."

Now showing 1 - 3 of 3

Comparison of Sebia capillary electrophoresis with the Bio-Rad VARIANT II HPLC in the evaluation of HbA2 in diagnosing beta thalassemia
(Faculty of Graduate Studies, University of Kelaniya Sri Lanka, 2022) Thilakarathne, S.; Wickramasinghe, M. G. C. N.; Perera, H. L.; Premawardhena, A. P.
The guideline for diagnosis of beta thalassemia trait in Sri Lanka defines low red cell indices (MCV<80 fl, MCH<27 pg) in FBC and HbA2>3.5% by quantification. Different cutoffs for HbA2 value are used in other countries (i.e. in India >4%). Thus, the precision of the HbA2 value is crucial for labelling a person as beta thalassemia trait. High-Performance Liquid Chromatography (HPLC) and capillary electrophoresis (CE) are two different techniques for quantifying HbA2 levels. This study aims to compare the HbA2 results of these two systems in individuals with varying HbA2 values and to assess the consistency when repeated of the two systems. The Bio-Rad VARIANT II HPLC (Bio-Rad, Hercules, USA) and the Sebia Capillarys CE (software version 9.3) analyzers were used as directed by the manufacturer. Using normal and pathological quality control materials, we determined the quality parameter, "between day precision", of both analyzers as per CLSI guidelines (EP15-A2 document). EDTA anticoagulated blood samples of patients (203) were analyzed by both methods during a 3- months period. Subjects (100) with HbA2 values between 1.8-3.3% were considered non-beta thalassemic, i.e. normal, while individuals (50) with HbA2 values >4.1% were categorized as beta thalassemia trait. We defined HbA2 levels as borderline (53) if they were between 3.4 and 4.0%. Incompatible FBC patterns and iron deficiency anemia was excluded from each group. Data analysis was performed using SPSS statistical software. HbA2 values by the CE method were slightly but significantly lower than those of the HPLC method, with a mean difference of 0.24 (Paired t-test; p <0.001). Also, HbA2 results by HPLC and CE methods showed a good relationship between each other (Pearson coefficient correlation; r was 0.98). We statistically analyzed this variation and relationship separately among normal, beta thalassemia trait and borderline groups. The variation in HbA2 value was high (mean difference; 0.27) among the normal group, while it was less (mean difference; 0.15) among beta thalassemia traits. The beta thalassemia trait group showed the highest positive relationship (r=0.92). The borderline group showed the least positive relationship (r=0.76). However, both analytical systems showed very close results (CV< 10%) when repeating the same sample between different days. This confirmed the excellent repeatability and acceptability of generated results by both analyzers. In conclusion, HbA2 values obtained from the two methods have a consistent and significant difference in normal, beta thalassemia trait and borderline samples. The variation in HbA2 values between CE and HPLC methods will make the accurate diagnosis of beta thalassemia traits more difficult based on a single reference cutoff value in the borderline group. Therefore, when issuing a diagnosis of beta thalassemia trait in borderline values, this machinerelated variation of the HbA2 level should be borne in mind.
Detection of β - Thalassemia carriers using data mining techniques
(The Institute of Applied Statistics, Sri Lanka, 2024) Subasinghe, G. K.; Chandrasekara, N. V.; Premawardhena, A. P.
Thalassemia, a genetic blood disorder, presents a significant challenge in Sri Lanka due to its high prevalence. Traditional methods of identifying tha-lassemia carriers, such as genetic and blood testing, are both costly and time-consuming, and potentially not available for certain demographic groups. However, there haven’t been many studies done on the efficacy of data mining models for thalassemia carrier detection, therefore the field is still in its in fancy. As such, evaluating their accuracy and utility in clinical practice is crucial. This study aims to develop a time-efficient model to detect the β-thalassemia carriers, which can reduce the time to take a decision and develop the built model as a decision support tool. Eight blood parameters - includ-ing RBC, HGB, HCT, MCV, MCH, MCHC, RDW, and HbA2 were selected based on literature. Two model-fitting approaches were introduced, each un-der different data selection methods: Method 1: Model fitting before handling the class imbalance problem and Method 02: Model fitting with random over-sampling technique. Support Vector Machine (SVM) and Probabilistic Neural Network (PNN) models were utilized for β-thalassemia carrier detection. Method 2 exhibited superior performance, especially with the PNN Model 2, achieving an impressive 98.75% overall classification accuracy. Moreover, the implemented PNN Model 2 could be utilized as an efficient decision-support tool, offering both time and cost savings in identifying β-thalassemia carriers. Nonetheless, for further investigation, consulting a medical expert is recommended.
Exploring data mining avenues in β-Thalassemia carrier identification
(Faculty of Science, University of Kelaniya Sri Lanka, 2023) Subasinghe, G. K.; Chandrasekara, N. V.; Premawardhena, A. P.
Thalassemia is a genetic blood disorder that affects the production of haemoglobin and is a global health problem. In comparison to many other nations in the region, Sri Lanka also has a high prevalence of thalassemia. The traditional methods for identifying thalassemia carriers, such as genetics and blood tests, are expensive and time-consuming and may not be available to all demographic groups. Nevertheless, the use of data mining models for thalassemia carrier detection is still in its infancy, and there are few studies on its efficacy. Therefore, it is vital to investigate the efficacy and accuracy of data mining approaches for detecting thalassemia carriers, as well as the viability of employing these methods in clinical practice. Thus, the objective of this study is to develop a time-efficient model to detect the β-thalassemia carriers, which can reduce the time to take a decision and develop the built model as a decision support tool. Also, the earlier detection will help individuals to refer to necessary treatments further. This study is carried out with the data obtained from Hemal's Adolescent and Adult Thalassemia Care Centre, Mahara, one of the treatments centres for thalassemia. As the study population, 343 individuals’ data values were considered from August 2019 to December 2019. When processing the dataset, 112 (36%) individuals were declared as β-thalassemia carriers, whereas 200 (64%) were identified as β- thalassemia non-carriers. Eight blood parameters, such as RBC, HGB, HCT, MCV, MCH, MCHC, RDW and HbA2 were identified by revealing the literature and the Chi-square and Mann- Whitney U tests were used to identify the association between the variables at 5% level of significance. A random over-sampling technique was used to overcome the class-imbalanced problem in the dataset, and based on that, model fitting was performed under the two data selection methods, i.e., Method 1: Model fitting before handling the class imbalance problem and Method 02: Model fitting with random over-sampling technique. Then 80% of the data was used for training the models, and 20% of the data was used for the evaluation. Support Vector Machine (SVM) and Probabilistic Neural Network (PNN) models were used to detect the β-thalassemia carriers. In comparison among methods, the better-performing models were given under Method 2, and the PNN model fitted under Method 2 (PNN Model 2) exhibits 98.75% overall classification accuracy. Here, the PNN model’s network architecture consisted of eight nodes in the input layer, 320 nodes in the pattern layer, two nodes in the summation layer, and two nodes in the output layer. Further, the fitted PNN Model 2 can be utilised as a cost-effective and timesaving option to detect β-thalassemia carriers in a few seconds with acceptable accuracy and can be implemented as a decision support tool. However, it is recommended to get advice from a medical doctor for further investigation.