A Regression Model For Alzheimer's Disease Progression Using The ADNI Database

Alzheimer's disease is a neurodegenerative disorder and the most common form of dementia. It affects approximately 50 million people worldwide, and to date, no definitive cure has been found. As one of the leading causes of death among individuals over the age of 65, early diagnosis is crucial, as it can significantly improve life expectancy and quality of life. In recent years, numerous machine learning techniques have been applied to various biomarkers to support the early detection of the disease. The objective of this project is to conduct an in-depth analysis of the ADNI database in order to study the characteristics of individuals affected by Alzheimer's at different stages of the disease, using machine learning methods. The results of this study demonstrated that it is possible to distinguish four distinct stages of Alzheimer's progression-from cognitively healthy individuals to those severely affected-rather than the commonly discussed three. Notably, the analysis also revealed that women are disproportionately impacted by the disease, accounting for nearly 80% of the affected population.