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. 2025 Apr 17;15(1):13359.
doi: 10.1038/s41598-025-97353-0.

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

Muhammad Umar Nasir #  1   2   3 , Muhammad Tahir Naseem #  4 , Taher M Ghazal  5 , Muhammad Zubair  2 , Oualid Ali  6 , Sagheer Abbas  7 , Munir Ahmad  8   9 , Khan Muhammad Adnan  10
Affiliations

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

Muhammad Umar Nasir et al. Sci Rep. .

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 achieved 99.34% accuracy on the private dataset for alpha thalassemia, while CNN reached 98.10% accuracy on the private dataset for beta-thalassemia. The superior performance on private datasets was attributed to better data quality and volume. This study highlights the effectiveness of deep learning in medical diagnostics, demonstrating that high-quality data can significantly enhance the predictive capabilities of AI models. By integrating CNN and XGBoost, this approach offers a robust method for detecting thalassemia, potentially improving early diagnosis and reducing disease-related mortality.

Keywords: Alpha thalassemia; Beta thalassemia; CNN; Case study; Deep learning; Thalassemia; XGBoost.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests. Conflicts of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Ethics approval and consent to participate: This study was approved by the Ethics committee of Riphah International University, Pakistan. Informed consent was obtained from all subjects. All the experimental procedures were performed by the standards of the Declaration of Helsinki 1964.

Figures

Fig. 1
Fig. 1
The proposed methodology to analyze the case studies of deep learning algorithms.
Fig. 2
Fig. 2
The proposed study of CNN architecture.
Fig. 3
Fig. 3
Training progress of CNN on private alpha thalassemia dataset.
Fig. 4
Fig. 4
Training progress of CNN on public alpha thalassemia dataset.
Fig. 5
Fig. 5
Training progress of CNN on private beta thalassemia dataset.
See this image and copyright information in PMC

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