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Identifying Patients with Familial Chylomicronemia Syndrome Using FCS Score-Based Data Mining Methods.
Ákos Németh1,2,3, Mariann Harangi1, Bálint Daróczy4,5
1Division of Metabolic Disorders, Department of Internal Medicine, Faculty of Medicine, University of Debrecen, H-4032 Debrecen, Hungary.
Journal of Clinical Medicine
|July 27, 2022
Summary
Familial chylomicronemia syndrome (FCS) prevalence in Central Europe is estimated at 19.4 per million. Machine learning models and the FCS score effectively identified patients, suggesting improved diagnostic accuracy with additional features.
Area of Science:
- Medical research
- Genetics
- Epidemiology
Background:
- Limited data exists on the prevalence of familial chylomicronemia syndrome (FCS) in Central Europe.
- Accurate diagnosis of FCS is crucial for patient management and understanding disease burden.
Purpose of the Study:
- To estimate the prevalence of FCS in Central Europe.
- To evaluate the diagnostic utility of the FCS score and data mining techniques for identifying FCS patients.
- To identify additional features that could enhance FCS diagnostic accuracy.
Main Methods:
- Analysis of 1,342,124 patient medical records.
- Calculation of the FCS score for each patient.
- Development and training of machine learning models (boosting trees, support vector machines, artificial neural networks) using data from diagnosed FCS patients.
Main Results:
- Identified 26 patients with an FCS score of ≥10, estimating FCS prevalence at 19.4 per million.
- Boosting tree models and support vector machines demonstrated high performance (AUC > 0.95) in patient recognition.
- Identified specific laboratory features that can potentially improve the FCS score's accuracy.
Conclusions:
- The estimated FCS prevalence in the studied region is higher than in other European countries.
- The FCS score is a valuable tool for identifying potential FCS cases.
- Incorporating additional laboratory features may further refine diagnostic accuracy for FCS.
