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Machine learning-assisted diagnosis classification of primary immune dysregulation using IDDA2.1 phenotype profiling.
Malte Schwitzkowski1, Sai Pavan Kumar Veeranki2, Benedikt N Seidel3
1Styrian Children's Cancer Research Unit for Cancer and Inborn Errors of the Blood and Immunity in Children, Division of Pediatric Hematology and Oncology, Department of Pediatric and Adolescent Medicine, Medical University of Graz, Graz, Austria.
The IDDA2.1 score aids in diagnosing inborn errors of immunity (IEIs) by analyzing immune dysregulation patterns. Machine learning models show promise for IEI classification, but larger datasets are needed for improved accuracy.
Area of Science:
- Immunology
- Computational Biology
- Genetics
Background:
- Primary immune disorders (PID) and inborn errors of immunity (IEIs) cause significant disease burden due to immune dysregulation.
- Conditions include autoimmunity, autoinflammation, allergy, and increased cancer risk.
Purpose of the Study:
- To evaluate the diagnostic utility of the 5-graded immune deficiency and dysregulation activity (IDDA2.1) score across various IEIs.
- To assess the score's ability to capture organ involvement and disease burden for IEI diagnosis.
Main Methods:
- Collected 1,043 IDDA score datasets from 825 patients across 89 IEIs.
- Utilized supervised machine learning (random forest, SVM, etc.) for classification and feature ranking.
- Employed unsupervised uniform manifold approximation and projection (UMAP) for visualizing disease clustering.
Main Results:
- IDDA score analysis confirmed internal consistency and reflected clinical IEI patterns.
- UMAP effectively distinguished IEIs based on IDDA2.1 profiles.
- Genetic disorder prediction achieved 73% overall accuracy, with 93% accuracy within the top 3 predictions.
Conclusions:
- The IDDA2.1 score and machine learning show diagnostic potential for IEIs.
- Enhanced accuracy requires larger, globally shared datasets and broader collaboration.
- Targeted clinical screening can be informed by random forest feature importance analysis.
