To date, reliable biomarkers remain unclear that could link functional connectivity to patients’ symptoms for detecting and predicting the process from normal aging to Alzheimer’s disease (AD) in elderly people with specific genotypes. To address this, individual-specific functional connectivity is constructed for elderly participants with/withoutAPOE ε4allele. Then, we utilize recursive feature selection-based machine learning to reveal individual brain-behavior relationships and to predict the symptom transition in different genotypes. Our findings reveal that compared with conventional atlas-based functional connectivity, individual-specific functional connectivity exhibits higher classification and prediction performance from normal aging to AD in bothAPOE ε4groups, while no significant performance is detected when the data of two genotyping groups are combined. Furthermore, individual-specific between-network connectivity constitutes a major contributor to assessing cognitive symptoms. This study highlights the essential role of individual variation in cortical functional anatomy and the integration of brain and behavior in predicting individualized symptoms.