Personalized Early Stage Alzheimer's Disease Detection: A Case Study of President Reagan's Speeches

TL;DR

This study explores machine learning techniques to detect early-stage Alzheimer's disease through linguistic analysis of President Reagan's speeches, identifying biomarkers and pinpointing onset years.

Contribution

It introduces an unsupervised clustering and anomaly detection approach for early AD detection using linguistic biomarkers in speech data.

Findings

Reagan showed early AD signs between 1983 and 1987.

The method accurately identifies speeches with early AD biomarkers.

Results align with prior statistical analyses.

Abstract

Alzheimer`s disease (AD)-related global healthcare cost is estimated to be $1 trillion by 2050. Currently, there is no cure for this disease; however, clinical studies show that early diagnosis and intervention helps to extend the quality of life and inform technologies for personalized mental healthcare. Clinical research indicates that the onset and progression of Alzheimer`s disease lead to dementia and other mental health issues. As a result, the language capabilities of patient start to decline. In this paper, we show that machine learning-based unsupervised clustering of and anomaly detection with linguistic biomarkers are promising approaches for intuitive visualization and personalized early stage detection of Alzheimer`s disease. We demonstrate this approach on 10 year`s (1980 to 1989) of President Ronald Reagan`s speech data set. Key linguistic biomarkers that indicate early-stage AD are identified. Experimental results show that Reagan had early onset of Alzheimer`s sometime between 1983 and 1987. This finding is corroborated by prior work that analyzed his interviews using a statistical technique. The proposed technique also identifies the exact speeches that reflect linguistic biomarkers for early stage AD.