ML-Based Quantitative Analysis of Linguistic and Speech Features Relevant in Predicting Alzheimer’s Disease

Abstract

Alzheimer’s disease (AD) is a severe neurological condition that affects numerous people globally with detrimental consequences. Detecting AD early is crucial for prompt treatment and effective management. This study presents a novel approach for detecting and classifying six types of cognitive impairment using speech-based analysis, including probable AD, possible AD, mild cognitive impairment (MCI), memory impairments, vascular dementia, and control. The method employs speech data from DementiaBank’s Pitt Corpus, which is preprocessed and analyzed to extract pertinent acoustic features. The characteristics are subsequently used to educate five machine learning algorithms, namely k-nearest neighbors (KNN), decision tree (DT), support vector machine (SVM), XGBoost, and random forest (RF). The effectiveness of every algorithm is assessed through a 10-fold cross-validation. According to the research findings, the suggested method based on speech obtains a total accuracy of 75.59% concerning the six-class categorization issue. Among the five machine learning algorithms tested, the XGBoost classifier showed the highest accuracy of 75.59%. These findings indicate that speech-based approaches can potentially be valuable for detecting and classifying cognitive impairment, including AD. The paper also explores robustness testing, evaluating the algorithms’ performance under various circumstances, such as noise variability, voice quality changes, and accent variations. The proposed approach can be developed into a noninvasive, cost-effective, and accessible diagnostic tool for the early detection and management of cognitive impairment.