Mostly affecting the senior population, Alzheimer's disease is a progressive neurological condition that seriously influences cognitive abilities, memory, and behavior. Timeliness of medical treatments and enhancement of the quality of life for afflicted people depend on early and precise diagnosis of Alzheimer's disease. Usually, diagnosis of Alzheimer's disease depends mostly on neuroimaging investigations, cognitive testing, and clinical assessments. Although these approaches offer insightful data, they may have restrictions like subjectivity, great expenses, and time-consuming procedures. Furthermore, these conventional methods might not always be sensitive enough to identify minute early illness symptoms. Consequently, the main issue covered in this work is the creation of a strong and accurate predictive model for Alzheimer's disease applying machine learning methods. Extraction of significant patterns and features from the multifarious and multidimensional DARWIN dataset—which includes a broad spectrum of biological, genetic, and clinical variables—is difficult. Therefore, this work intends to create a strong prediction model with the DARWIN dataset, a large collection of clinical, genetic, and biological information. The study aims to automatically evaluate this complicated dataset by using machine learning techniques, therefore uncovering nuanced patterns and relationships beyond human capabilities. The aim is to develop a precise prediction model able to detect possible Alzheimer's instances depending on several factors. Promising more sensitive, effective, and reasonably priced ways, this project marks a major shift from conventional diagnostic tools. Machine learning models help to early diagnosis by automating the analytical process, therefore supporting prompt medical interventions and a better knowledge of Alzheimer's disease. In the end, the suggested approach has the ability to transform Alzheimer's diagnosis, so improving patient care and enabling research for sensible therapies and preventative actions.

alzheimer’s disease, security and data protection, darwin, neurodegenerative, machine learning.