Thalaseemia Prediction Using Deep Neural Networks: A Step Towards Personalized Medicine
V. Pream Sudha, S. Karpagavalli, K. Sathiyakumari, A.C. Sountharraj · 2025
Thalaseemia is a series of genetic hemolytic diseases brought on by defective hemoglobin synthesis. It is prevalent in many Asian, African, and Mediterranean nations. Four million Indians are Thalaseemia carriers, and more than 100,000 are actively combating the condition, according to the World Health Organization. It is specifically taken under investigation because cases of Thalassemia that have clinical, hematologic characteristics cannot be effectively separated from cases of sickle cell anemia and are misdiagnosed as such. The precise profiles of the genetic and pathophysiological mechanisms of patients with Thalassemia are not yet well defined, despite the fact that they are clinically treated identically. Even among people with the same genotype, there are differences in the severity of the clinical phenotype. The frequency of complications changes over time, both within and between individuals. Patients display a striking variation in severity, from a milder clinical course to severe transfusion dependency and progressive organ destruction. This study focuses on the creation of a discriminative model for Thalaseemia classification. In this study, the prediction of Thalassemia is modeled as a pattern classification issue, and the models are constructed using deep learning approaches. The hybrid CNN-Long Short Term Memory (LSTM) based Thalaseemia prediction model has demonstrated improved performance, according to the performance evaluation of these models.