AI-Driven Classification of Alzheimer's and Parkinson's Disease Using Phonetic Speech Patterns

Arkoneil Ghosh

Abstract: Neurodegenerative disorders such as Alzheimer's and Parkinson's are challenging to detect early due to their gradual onset. This study investigates the use of machine learning algorithms to identify these conditions based on phonetic features in speech. By analyzing vocal attributes, such as fluency, articulation, and acoustic variation; this research aims to establish non-invasive diagnostic models. Principal Component Analysis (PCA) was used for feature selection, while Random Forest and Support Vector Machine (SVM) classifiers were deployed for detection accuracy. Results show promising accuracy levels, particularly in the Alzheimer?s model, highlighting the potential of AI in enhancing early clinical screening for cognitive decline.

Keywords: phonetic speech analysis, Alzheimer's classification, Parkinson's detection, machine learning models, AI in diagnostics

How to Cite?: Arkoneil Ghosh, "AI-Driven Classification of Alzheimer's and Parkinson's Disease Using Phonetic Speech Patterns", Volume 14 Issue 8, August 2025, International Journal of Science and Research (IJSR), Pages: 221-231, https://www.ijsr.net/getabstract.php?paperid=SR25803144753, DOI: https://dx.doi.org/10.21275/SR25803144753

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