Journal of thrombosis and haemostasis : JTH · 2024
Application of machine learning approaches for predicting hemophilia A severity.
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Rawal Atul, Kidchob Christopher, Ou Jiayi, Sauna Zuben E
Аннотация
BACKGROUND: Hemophilia A (HA) is an X-linked congenital bleeding disorder, which leads to deficiency of clotting factor (F) VIII. It mostly affects males, and females are considered carriers. However, it is now recognized that variants of F8 in females can result in HA. Nonetheless, most females go undiagnosed and untreated for HA, and their bleeding complications are attributed to other causes. Predicting the severity of HA for female patients can provide valuable insights for treating the conditions associated with the disease, such as heavy bleeding.
OBJECTIVES: To predict the severity of HA based on F8 genotype using a machine learning (ML) approach.
METHODS: Using multiple datasets of variants in the F8 and disease severity from various repositories, we derived the sequence for the FVIII protein. Using the derived sequences, we used ML models to predict the severity of HA in female patients.
RESULTS: Utilizing different classification models, we highlight the validity of the datasets and our approach with predictive F1 scores of 0.88, 0.99, 0.93, 0.99, and 0.90 for all the validation sets.
CONCLUSION: Although with some limitations, ML-based approaches demonstrated the successful prediction of disease severity in female HA patients based on variants in the F8. This study confirms previous research findings that ML can help predict the severity of hemophilia. These results can be valuable for future studies in achieving better treatment and clinical outcomes for female patients with HA, which is an urgent unmet need.
OBJECTIVES: To predict the severity of HA based on F8 genotype using a machine learning (ML) approach.
METHODS: Using multiple datasets of variants in the F8 and disease severity from various repositories, we derived the sequence for the FVIII protein. Using the derived sequences, we used ML models to predict the severity of HA in female patients.
RESULTS: Utilizing different classification models, we highlight the validity of the datasets and our approach with predictive F1 scores of 0.88, 0.99, 0.93, 0.99, and 0.90 for all the validation sets.
CONCLUSION: Although with some limitations, ML-based approaches demonstrated the successful prediction of disease severity in female HA patients based on variants in the F8. This study confirms previous research findings that ML can help predict the severity of hemophilia. These results can be valuable for future studies in achieving better treatment and clinical outcomes for female patients with HA, which is an urgent unmet need.