A diagnostic application for Parkinson's disease is disclosed. The diagnostic application for Parkinson's disease diagnoses Parkinson's disease by extracting acoustic features of voice input to a smart terminal through frequency conversion of the voice, the acoustic features including an average, a deviation, a frequency variation (F0 variation), a harmonics to noise ratio (HNR), intensity of the voice, jitter of the voice, and shimmer of the voice, subdividing a voice recognition measurement scheme according to speaker independence, a pronunciation pattern, and the number of vocabularies based on Parkinson's disease diagnosis data through an application installed in the smart terminal, using the extracted acoustic features, and canceling noise introduced during voice recording through a cepstrum.
PARKINSON'S DISEASE DIAGNOSTIC APPLICATION