Precise spatiotemporal regulation of splicing is mediated by splicing cis-elements on pre-mRNA. Single-nucleotide variations (SNVs) affecting intronic cis-elements possibly compromise splicing, but no efficient tool has been available to identify them. Following an effect-size analysis of each intronic nucleotide on annotated alternative splicing, we extracted 105 parameters that could affect the strength of the splicing signals. However, we could not generate reliable support vector regression models to predict the percent-splice-in (PSI) scores for normal human tissues. Next, we generated support vector machine (SVM) models using 110 parameters to directly differentiate pathogenic SNVs in the Human Gene Mutation Database and normal SNVs in the dbSNP database, and we obtained models with a sensitivity of 0.800±0.041 (mean and s.d.) and a specificity of 0.849±0.021. Our IntSplice models were more discriminating than SVM models that we generated with Shapiro-Senapathy score and MaxEntScan::score3ss. We applied IntSplice to a naturally occurring and nine artificial intronic mutations in RAPSN causing congenital myasthenic syndrome. IntSplice correctly predicted the splicing consequences for nine of the ten mutants. We created a web service program, IntSplice (http://www.med.nagoya-u.ac.jp/neurogenetics/IntSplice) to predict splicing-affecting SNVs at intronic positions from -50 to -3.