Alzheimer’s Disease Diagnosis Using Demographic Data: A Preprocessing and Analytics Statistical Approach

Alzheimer’s disease (AD) is one of the leading causes of brain degeneration, memory impairment and physical functionality of elderly people around the world. In addition, this disease might impact patients’ family members and the financial, economic and social aspects of their societies. Such a prevailing disease necessitates the diagnosis and prognosis of its inception, development and progression as early as possible. Researchers have recently investigated different statistical, data analytics and machine learning approaches to detect such disease at an earlier stage in order to help neurologists to detect the disease earlier, before its progression and with minimal harm. This paper reports the empirical study employing data analytics and statistics performed on the Alzheimer’s Disease Neuroimaging Initiative longitudinal data repository (ADNI) to assess the impact of demographic factors such as gender, age, education, race, ethnicity and marital status on AD progression. These factors often influence AD through its progression from cognitively normal (CN) status to mild cognitive impairment (MCI) to AD. The study utilises statistical techniques including descriptive analytics, cross-tabulation distributions, Chi-square tests, ANOVA analyses and box plot visualisations on the ADNI dataset to assess the effect of demographic factors on AD diagnosis. Statistical analysis results reveal significant relationships between demographic features (age, gender, education, ethnicity, race and marital status) and the progression of the disease through the three clinical states (CN, MCI and Dementia) that can significantly assist in the determination of early AD.