Detection of Neurocognitive Impairment in the Elderly Using Deep Learning Algorithms

Detection Of Neurocognitive Impairment In The Elderly Using Deep Learning Algorithms

Authors- K. Purushothaman, Assistant Professor Dr.Lipsa Nayak

Abstract-– With the aging global population, neurocognitive disorders such as Alzheimer’s and various forms of dementia are becoming increasingly prevalent. These conditions not only affect patients’ quality of life but also pose significant challenges to healthcare systems. Early detection plays a crucial role in improving patient outcomes, yet traditional diagnostic methods often fail to identify the earliest signs of cognitive decline. This study proposes a deep learning-based approach that leverages both structured clinical data and MRI imaging to enhance diagnostic accuracy. We utilize the TabTransformer model to analyze structured clinical data, including demographic, lifestyle, and cognitive assessments, following an in-depth Exploratory Data Analysis (EDA) to better understand key features. In parallel, the ConvNeXt model processes MRI images to detect structural brain abnormalities associated with neurocognitive impairment. A multimodal learning strategy integrates insights from both models, allowing for a more comprehensive assessment of cognitive health. This approach improves predictive performance and provides a practical, AI- driven solution for early detection. Experimental results demonstrate that combining clinical and imaging data enhances diagnostic reliability, supporting healthcare professionals in making more informed decisions.

DOI: /10.61463/ijset.vol.13.issue2.362