NeuroImage · 2026
Converse or reverse? Machine-learning modeling for disease progression: A study based on Alzheimer's disease continuum cohort.
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Huang Yujing, Zhang Hao, Ma Buqing, Yu Zhe, Dai Shenyi, Cheng Lu, Su Li, Alzheimer’s Disease Neuroimaging Initiative (ADNI), Yang Gaoyi, Ma Qingguo
Аннотация
INTRODUCTION: Longitudinal trajectories from healthy aging to Mild Cognitive Impairment and Alzheimer's Disease involve complex mechanisms.
METHODS: We evaluated five machine learning approaches (Random Forest, Support Vector Machines, Radial Basis Function Networks, Backpropagation Networks, Convolutional Neural Network) to assess the importance of potential predictive markers across the health-to-dementia continuum. Using the ADNI cohort across four phases (ADNI1, ADNIGO, ADNI2, ADNI3), we analyzed participants with distinct trajectories: stable, convertible, and reverse progression.
RESULTS: Random Forest outperformed other models across key effectiveness metrics and achieved a macro-averaged sensitivity of 70.8 % and specificity of 96.8 % across all participant groups. Random Forest identified visuospatial and memory-related cognitive dysfunction as key predictive clinical features and several amyloid-related neuroimaging biomarkers - including temporal variations of amyloid uptake within inferior lateral ventricles, para-hippocampus-for classifying participant groups. Additionally, plasma APOE4 and long neurofilament light chain levels emerged as promising predictors for tracking progression.
CONCLUSION: These findings highlight the potential of machine learning in classifying disease trajectories.
METHODS: We evaluated five machine learning approaches (Random Forest, Support Vector Machines, Radial Basis Function Networks, Backpropagation Networks, Convolutional Neural Network) to assess the importance of potential predictive markers across the health-to-dementia continuum. Using the ADNI cohort across four phases (ADNI1, ADNIGO, ADNI2, ADNI3), we analyzed participants with distinct trajectories: stable, convertible, and reverse progression.
RESULTS: Random Forest outperformed other models across key effectiveness metrics and achieved a macro-averaged sensitivity of 70.8 % and specificity of 96.8 % across all participant groups. Random Forest identified visuospatial and memory-related cognitive dysfunction as key predictive clinical features and several amyloid-related neuroimaging biomarkers - including temporal variations of amyloid uptake within inferior lateral ventricles, para-hippocampus-for classifying participant groups. Additionally, plasma APOE4 and long neurofilament light chain levels emerged as promising predictors for tracking progression.
CONCLUSION: These findings highlight the potential of machine learning in classifying disease trajectories.