A comprehensive case study of deep learning on the detection of alpha thalassemia and beta thalassemia using public and private datasets.

ABSTRACT: This study explores the performance of deep learning models, specifically Convolutional Neural Networks (CNN) and XGBoost, in predicting alpha and beta thalassemia using both public and private datasets. Thalassemia is a genetic disorder that impairs hemoglobin production, leading to anemia and other health complications. Early diagnosis is essential for effective management and prevention of severe health issues. The study applied CNN and XGBoost to two case studies: one for alpha-thalassemia and the other for beta-thalassemia. Public datasets were sourced from medical databases, while private datasets were collected from clinical records, offering a more comprehensive feature set and larger sample sizes. After data preprocessing and splitting, model performance was evaluated. XGBoost ach

SUBMITTER: Nasir MU 

PROVIDER: S-EPMC12006322 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature