Exploring Alzheimer’s Disease Risk Prediction: A Data-Driven Approach with RandomForestClassifier

In this work, we employ a sophisticated machine learning technique based on the RandomForestClassifier model to examine Alzheimer’s disease risk prediction. The “oasis_cross-sectional.csv” and “oasis_longitudinal.csv” full datasets, which comprise both cross-sectional and longitudinal data, serve as the basis for our study. Our research project’s major purpose is to create and assess a prediction model that may identify a person’s chance of getting Alzheimer’s disease based on clinical and demographic characteristics. Our method entails substantial data preparation, including feature engineering, imputation of missing values, and normalization. Feature engineering is a discrete stage. Stratified cross-validation approaches are used to train and modify the RandomForestClassifier model, establishing the validity of the performance metrics. Several important results from our work include evaluations of model correctness, a thorough assessment of prediction abilities, and insights into critical components that contribute to the risk assessment of Alzheimer’s disease. In this chapter, we underline how machine learning may increase Alzheimer’s disease research and clinical practice, and we analyze the implications of our results for early detection and therapeutic possibilities. This paper contributes a large quantity of information to the growing corpus in the realm of Alzheimer’s disease prediction. It illustrates the advantages of data-driven methods and the importance of applying powerful machine learning algorithms in order to enhance healthcare results.