To enhance the accuracy and reliability of Alzheimer's disease (AD) detection models, a multifaceted approach incorporating various cognitive assessments and data sources is essential. Beyond spontaneous speech analysis, other cognitive assessments such as neuropsychological tests, memory recall tasks, and executive function evaluations can provide valuable insights into cognitive performance. For instance, standardized tests like the Mini-Mental State Examination (MMSE) or the Montreal Cognitive Assessment (MoCA) can yield quantitative scores that reflect different cognitive domains, allowing for a more nuanced understanding of cognitive impairment.
Additionally, integrating multimodal data sources, such as neuroimaging (e.g., MRI, PET scans), genetic information, and biomarkers (e.g., amyloid and tau levels), can significantly enrich the dataset. These sources can help identify structural and functional brain changes associated with AD, providing a more comprehensive view of the disease's progression. Furthermore, wearable devices that monitor daily activities and cognitive engagement can offer real-time data on behavioral patterns, which may correlate with cognitive decline. By combining these diverse data sources, researchers can develop more robust machine learning models that account for the complexity of cognitive impairments in AD, ultimately leading to improved detection and intervention strategies.