Thalassemia is a group of inherited blood disorders that happen when
hemoglobin, the protein in red blood cells that carries oxygen, is not made
enough. It is found all over the body and is needed for survival. If both
parents have thalassemia, a child's chance of getting it increases. Genetic
counselling and early diagnosis are essential for treating thalassemia and
stopping it from being passed on to future generations. It may be hard for
healthcare professionals to differentiate between people with thalassemia
carriers and those without. The current blood tests for beta thalassemia
carriers are too expensive, take too long, and require too much screening
equipment. The World Health Organization says there is a high death rate for
people with thalassemia. Therefore, it is essential to find thalassemia
carriers to act quickly. High-performance liquid chromatography (HPLC), the
standard test method, has problems such as cost, time, and equipment needs. So,
there must be a quick and cheap way to find people carrying the thalassemia
gene. Using federated learning (FL) techniques, this study shows a new way to
find people with the beta-thalassemia gene. FL allows data to be collected and
processed on-site while following privacy rules, making it an excellent choice
for sensitive health data. Researchers used FL to train a model for
beta-thalassemia carriers by looking at the complete blood count results and
red blood cell indices. The model was 92.38 % accurate at telling the
difference between beta-thalassemia carriers and people who did not have the
disease. The proposed FL model is better than other published methods in terms
of how well it works, how reliable it is, and how private it is. This research
shows a promising, quick, accurate, and low-cost way to find thalassemia
carriers and opens the door for screening them on a large scale.

使用联邦学习技术，本研究提出了一种新的、快速、准确和低成本的方法来检测地中海贫血基因携带者，该方法通过分析完整的血液计数结果和红细胞指数来训练一个模型，92.38% 的准确率能区分患有该疾病的贝塔地中海贫血携带者和正常人，相比使用高效液相色谱法等其他已发布的方法，该 FL 模型的性能更好、更可靠且更保护个人隐私