Three Steps Novel Hard Margin Ensemble Machine Learning Method Classifies Uncertain Mefv Gene Variants

IntroductionThe International Study Group for Systemic Autoinflammatory Diseases (INSAID) consensus criteria revealed that the clinical outcomes of more than half of the MEFV gene variants are uncertain. We aimed to detect more accurate classifications of MEFV variants while simultaneously reducing MEFV variant uncertainty. Material-MethodsWe extracted variants of the MEFV gene from the infevers database. We then determined the optimal number of in silico instruments for our model. On the training dataset, we implemented seven machine learning algorithms on MEFV gene variants with known clinical effects. We evaluated the effectiveness of our model in three steps: First, we performed machine-learning algorithms on the training dataset and implemented those with a prediction accuracy of greater than 90 percent. Second, we compared our gene-level and protein-level prediction results. Finally, we compared our prediction results to clinical outcomes. ResultsOur analysis included 266 of 381 MEFV gene variants and four computational tools (Revel, SIFT, MetaLR, and FATHMM). In our training dataset, the accuracy of three machine learning algorithms (RF: 100%, CRAT: 100%, and KNN: 91%) exceeded the threshold value. Thus, the dataset contained 134 likely pathogenic (LP) variants and 132 likely benign (LB) variants. We found that B30.2 domain variants were 2.5 times more likely to be LP than LB ({chi}2:12.693, p < 0.001, OR: 2.595 [1.532-4.132]. DiscussionConsidering that the clinical effects of 60% of MEFV gene variants have not yet been determined, a combined evaluation of our methods and patients clinical manifestations significantly simplifies the interpretation of unknown variants.