With many amyloidosis-associated missense mutations still unidentified and early diagnostic methods largely unavailable, there is an urgent need for a reliable computational approach to predict hereditary amyloidoses from gene sequencing data. Progress has been made in predicting amyloidosis-triggering sequences within intrinsically disordered regions. However, some diseases are caused by mutations in amyloidogenic regions within structured domains that must unfold for amyloid formation. Accurate prediction of amyloidogenic regions requires tools for detecting amyloidogenicity and assessing mutation effects on protein stability. We developed datasets of mutations linked to hereditary ATTR cardiomyopathy and others likely unrelated, evaluating TTR mutants with amyloidogenicity and stability predictors. Notably, the stability predictors consistently indicated that ATTR-related mutations tend to destabilize the TTR structure more than non-ATTR-associated mutations. Using these datasets and newly generated mutation features, we developed a machine learning model SDAM-TTR to predict mutations leading to ATTR cardiomyopathy.