A number of neurologic diseases, including a form of amyotrophic lateral sclerosis and others associated with expanded nucleotide repeats have an unconventional form of translation called repeat-associated non-AUG (RAN) translation. Repeat protein products accumulate and are hypothesized to contribute to disease pathogenesis. It has been speculated that the repeat regions in the RNA fold into secondary structures in a length-dependent manner, promoting RAN translation. Additionally, nucleotides that flank the repeat region, especially ones closest to the initiation site, are believed to enhance translation initiation. Recently, a machine learning model based on a large number of flanking nucleotides has been proposed for identifying translation initiation sites. However, most likely due to its extensive feature selection and limited training data, the model has diminished predictive power. Here, we overcome this limitation and increase prediction accuracy by a) capturing the effect of nucleotides most critical for translation initiation via feature reduction, b) implementing an alternative machine learning algorithm better suited for limited data, c) building comprehensive and balanced training data (via sampling without replacement) that includes previously unavailable sequences, and, d) splitting ATG and near-cognate translation initiation codon data to train two separate models. We also design a supplementary scoring system to provide an additional prognostic assessment of model predictions. The resultant models have high performance, with 85.00-87.79% accuracy exceeding that of the previously published model by >18%. The models presented here are then used to identify translation initiation sites in genes associated with a number of neurologic repeat expansion disorders. The results confirm a number of experimentally discovered sites of translation initiation upstream of the expanded repeats and predict many sites that are not yet established.