Unsupervised Cognitive Impairment Detection Using Convolutional Autoencoders and Isolation Forest - Salekshahrezaee, Zahra; Khoshgoftaar, Taghi M. (Proceedings of the IEEE International Conference on Information Reuse and Integration (Online), 2025)
Early detection of cognitive impairment is essential for timely intervention and treatment of conditions such as Alzheimer's disease. Supervised machine learning models require labeled data for training, which is often scarce, costly to obtain, and subject to diagnostic uncertainty. In this work, we propose CAE-IF, a fully unsupervised hybrid approach that combines Convolutional Autoencoders (CAE) for feature extraction with Isolation Forest (IF) for anomaly detection. Our method is evaluated on a real-world, imbalanced cognitive dataset derived from the Health and Retirement Study (HRS). CAE-IF consistently outperforms two baseline unsupervised models, Local Outlier Factor (LOF) and Isolation Forest, across key evaluation metrics. For AUPRC, CAE-IF achieves 0.3042 compared to 0.2164 for LOF and \text{0. 2 4 9 0} for IF. For F1-score, CAE-IF achieves 0.3380, outperforming LOF ( \text{0. 2 0 8 9} ) and IF ( \text{0. 2 6 4 0} ). For MCC, CAE-IF scores 0.1818, higher than LOF (0.0323) and IF (0.0892). These results demonstrate the superior performance of CAE-IF in detecting cognitive impairment under class imbalance. These results indicate that CAE-IF can serve as a useful tool for early screening of cognitive impairment using survey-based data.