ShreyanshJoshi/ML-based-Analysis-to-Identify-Speech-Features-Relevant-in-Predicting-Alzheimer-s
ML and DL based models that predict (and classify) whether a person has Alzheimer's Disease or not by analyzing his/her speech.
This project helps clinicians and researchers identify early indicators of Alzheimer's disease through speech analysis. It takes transcribed speech data and processes it to extract linguistic features related to syntax, word usage, and fluency. The outcome is a prediction of whether a person shows signs of Alzheimer's and highlights which specific speech features are most important in that assessment, which could be useful for neurologists or speech-language pathologists.
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Use this if you need a non-invasive, quick, and scalable method to assess the likelihood of Alzheimer's disease by analyzing a person's speech patterns.
Not ideal if you need a definitive medical diagnosis rather than a predictive screening tool based on speech, or if you lack transcribed speech data for analysis.
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Sep 04, 2021
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