Anushka Paulchoudhury

Award-Winning Machine Learning Predictive Algorithm of Alzheimer's Disease from the Analysis of Semantics and Early Linguistic Variables

Started Aug. 3, 2021

Abstract or project description

Over 6 million Americans are living with Alzheimer’s disease, and dementia deaths have seen an increase of 16% during the COVID-19 pandemic. Dementia was predicted to cost Americans $355 billion by the end of 2021. With an increasing percentage of cases and rising healthcare costs, the need for accessible prediction of dementia utilizing the analysis of properties of digital biomarkers is necessary. The majority of individuals who experience the symptoms of Alzheimer’s are 65 years or older. Starting from the age of 65 years, the risk of succumbing to Alzheimer's significantly increases - doubling every 5 years. There is an evident overlap between the high-risk age groups of COVID-19 and Alzheimer’s Disease. Thus, the demand for digital prediction tools has unquestionably increased. The purpose of conducting this research is to employ digital and physically contact-free technology to construct an artificial intelligence model to identify early symptoms of Alzheimer’s Disease. Data was collected from fluency data from UCSD. The machine learning algorithm that was most effective at predicting the advancement of Alzheimer’s Disease in patients who are able to function cognitively normally has an accuracy percentage of 93%. Further research into this area of building digital Alzheimer’s Disease prediction tools will help aid the early diagnosis of dementia, lower the burden on medical professionals, and assist in meeting the rising healthcare needs of countries with underdeveloped healthcare systems.