Analysis of Clinical, Genetic, and Demographic Data for Prediction of Alzheimer's Disease with Machine Learning

In the context of the ageing of the global population and the increasing prevalence of Alzheimer's disease (AD), early and accurate diagnosis is crucial for effective management and treatment. Using Exploratory Data Analysis (EDA) we dissect the complex relationships between various risk factors and disease progression, establishing a basis for our predictive modelling. Uncovering critical insights, and emphasizing the importance of adopting a multidimensional approach to analyze diverse datasets effectively, our study highlights the critical role of data quality and diversity in improving model performance. The fundamental aspect of our analysis focuses on the predictive power of combining different data types, which traditionally include clinical parameters, genetic markers, and demographic and lifestyle data.The research highlights the application of machine learning (ML) techniques for early detection and predictive analysis of Alzheimer's disease, demonstrating the enormous potential of artificial inelegance in transforming healthcare diagnostics. The study conducted a comparative analysis of various ML algorithms and evaluated their efficiency in disease detection.This research contributes to the academic discourse on the diagnosis of Alzheimer's disease and provides practical insights for the application of artificial intelligence and machine learning in clinical practice.