AUTHOR=Wang Lei , Guo Jian , Tian Zhuang , Seery Samuel , Jin Ye , Zhang Shuyang 

TITLE=Developing a Hybrid Risk Assessment Tool for Familial Hypercholesterolemia: A Machine Learning Study of Chinese Arteriosclerotic Cardiovascular Disease Patients

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.893986

DOI=10.3389/fcvm.2022.893986

ISSN=2297-055X

ABSTRACT=Background: Familial hypercholesterolemia (FH) is an autosomal-dominant genetic disorder with a high risk of premature arteriosclerotic cardiovascular disease (ASCVD). There are many alternative risk assessment tools e.g., DLCN, although their sensitivity and specificity varies among specific populations. We aimed to assess risk discovery performance of a hybrid model consisting of existing FH risk assessment tools and machine learning (ML) methods, based on the Chinese ASCVD patients. 
Methods: 5,597 primary ASCVD patients were assessed for FH risk using 11 tools. The three best-performing tools were hybridized through a voting strategy. ML models were set according to hybrid results to create a hybrid FH risk assessment tool (HFHRAT). PDP and ICE were adopted to interpret black-box features.
Results: After hybridizing the mDLCN, Taiwan criteria and DLCN, the HFHRAT was taken as stacking ensemble method (AUC_class[94.85±0.47], AUC_prob[98.66±0.27]). Interpretation of HFHRAT suggests that patients <75 years of age with LDL-c >4mmol/L were more likely to be at risk of developing FH.
Conclusion: The HFHRAT has provided a median of the three tools, which could reduce the false-negative rate associated with existing tools and prevent the development of atherosclerosis. The hybrid tool could satisfy the need for a risk assessment tool for specific populations.