  This study is based on the Alzheimer's Disease Neuroimaging Initiative (ADNI)
dataset and aims to explore early detection and disease progression in
Alzheimer's disease (AD). We employ innovative data preprocessing strategies,
including the use of the random forest algorithm to fill missing data and the
handling of outliers and invalid data, thereby fully mining and utilizing these
limited data resources. Through Spearman correlation coefficient analysis, we
identify some features strongly correlated with AD diagnosis. We build and test
three machine learning models using these features: random forest, XGBoost, and
support vector machine (SVM). Among them, the XGBoost model performs the best
in terms of diagnostic performance, achieving an accuracy of 91%. Overall, this
study successfully overcomes the challenge of missing data and provides
valuable insights into early detection of Alzheimer's disease, demonstrating
its unique research value and practical significance.
