Intelligent Diagnosis of Alzheimer's Disease Based on Machine Learning

TL;DR

This paper develops machine learning models, especially XGBoost, for early Alzheimer's detection using ADNI data, addressing missing data challenges and identifying key diagnostic features.

Contribution

It introduces innovative data preprocessing techniques and compares multiple models, highlighting the effectiveness of XGBoost in early AD diagnosis.

Findings

XGBoost achieved 91% accuracy in AD diagnosis.

Effective handling of missing data improves model performance.

Identified features strongly correlated with Alzheimer's diagnosis.

Abstract

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.