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Hemostasis is a crucial process that prevents excessive blood loss from damaged blood vessels. It involves various mechanisms such as vasoconstriction, platelet adhesion and activation, and fibrin formation. The importance of each mechanism depends on the type of vessel injury. In contrast, thrombosis is the abnormal formation of a blood clot within the blood vessels, leading to potential complications if the clot obstructs blood flow. Thrombosis can be caused by increased coagulability of the...
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Related Experiment Video

Updated: May 10, 2025

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
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Predicting Inhibitor Development in Hemophilia 'A' using Machine Learning: A Comprehensive Approach to Data

Vikalp Kumar Singh1, Maheshwari Prasad Singh1

  • 1Department of Computer Science and Engineering, National Institute of Technology, Patna, Bihar 800005, India.

Current Pharmaceutical Biotechnology
|April 23, 2025
PubMed
Summary
This summary is machine-generated.

This study developed an AI model to predict Factor VIII inhibitors in Hemophilia A patients, achieving 97.37% accuracy. The model identifies key biomarkers for personalized treatment strategies.

Keywords:
Hemophilia ASHAP.XAIblood disorderdata balancinginhibitorsmachine learningrandom over sampling

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Area of Science:

  • Computational biology and bioinformatics
  • Genetics and genomics
  • Medical artificial intelligence

Background:

  • Hemophilia A (HA) is a genetic bleeding disorder caused by Factor VIII (FVIII) deficiency.
  • FVIII inhibitors develop in some HA patients during treatment, complicating management.
  • Predicting inhibitor development is challenging due to data imbalance and complexity.

Purpose of the Study:

  • To develop a machine learning/AI approach for predicting FVIII inhibitors in HA patients.
  • To identify biomarkers associated with inhibitor development.
  • To address data imbalance and improve prediction accuracy.

Main Methods:

  • Data sanitization and encoding were performed on the CHAMP dataset.
  • Random Over-sampling (ROS) addressed data imbalance.
  • Multiple machine learning models (Random Forest, XGBoost, etc.) were trained and evaluated using stratified k-fold cross-validation and GridSearchCV.
  • SHAP (SHapley Additive exPlanations) was used for explainable AI analysis.

Main Results:

  • The Random Forest model achieved 97.37% accuracy, outperforming other classifiers.
  • SHAP analysis identified key predictive variables including Clinical Severity, Variant Type, Exon, and HGVS cDNA.
  • Biomarkers associated with FVIII inhibition were identified.

Conclusions:

  • The study presents a breakthrough in early prediction of FVIII inhibitors in HA patients.
  • The integrated approach (preprocessing, Random Forest, SHAP) offers a novel solution for personalized treatment.
  • This facilitates the development of targeted and effective therapies for HA patients.