Abstract: The techniques described herein relate identification of disease-causing genetic variant by machine learning classification. The techniques may include receiving a training dataset of predetermined variants associated with disease. A hyperplane is identified having a maximum margin between points of the dataset. Patient input data is received including an observed variant of a gene. Features of the observed variant are selected, and a score is determined The score is determined using Support Vector Machine algorithms based on an observation of a novel non-linear relationship with the selected features of the observed variant. The observed variant may be classified based on the score indicating a distance of the observed variant from the identified hyperplane.