[Abstract] Objectives: To determine whether the combination of neurocognitive assessment and key biomarker data can improve the accuracy of using MRI image data to predict Alzheimer disease (AD) status and conversion. Methods: Data were collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) during 2004-2018. Samples with complete MRI image data, cognitive assessment data and biological measures were screened from the raw data. Seven brain volumetric features including ventricular, hippocampus, whole brain, entorhinal cortex, fusiform gyrus, middle temporal gyrus, and intracerebral volumes were extracted from MRI by toolbox FreeSurfer. Cognitive assessment scale included the MMSE and ADAS-Cog13 scale. Biological measurement data included four biomarkers, i.e., Abeta peptide, Tau protein, p-Tau protein and ApoE4 genotype. Based on the baseline data of 783 samples, logistic regression model was established for classification of disease development stages. Based on the longitudinal data of 352 samples with MCI status, a binary logistic regression was established for distinguishing converted patients from non-converted patients. We integrated cognitive data, and biomarkers with the brain image data, randomly divided the data set through a cross-validation method, and calculated accuracy, recall, precision, F1 score, and the area under the ROC curve. Results: For the classification of Alzheimer disease development stage, combining brain image data, cognitive data and biological measures achieved accuracy rates of 100% (AD vs. Normal), 77.18% (MCI vs. Normal) and 89.58% (AD vs. MCI)); the areas under the ROC curves are 100% (AD vs. Normal), 85.52% (MCI vs. Normal) and 96.39% (AD vs. MCI) respectively; The AUCs for distinguishing Normal, MCI, and AD from the other two categories are 88.30%, 81.00% and 97.26% respectively, which are significantly higher than the classification performance using only brain image data. For classification of MCI conversion, the brain image data combined with the cognitive data can maximize the accuracy rate, from 86.69% to more than 90%; the corresponding AUC increased from 89.21%, which only use the brain image data to 94.06%. Conclusion Combining data from multiple sources can improve the classification and prediction accuracy of Alzheimer disease status and conversion, thus provide theoretical support for clinical practice in early diagnosis of the Alzheimer disease.