<p>With the intensification of global population aging, the incidence of cognitive impairment such as dementia continues to rise. The Activity of Daily Living (ADL) scale can help to assess daily living functions and early screen for dementia, which is crucial for delaying disease progression and improving the quality of life in older adults. As advanced ADL assessment tools continue to be developed and improved, how to evaluate their effectiveness is particularly important. However, most studies have assessed these tools from a single perspective and have often failed to examine the contribution of individual items within the scales. Therefore, we propose the Statistical and Machine Learning-based Advanced ADL scale Effectiveness Assessment Method (SML-AAEA) to evaluate the psychometric properties and early dementia screening ability of ADL assessment tool, including: (1) scale design and data collection based on traditional scales and advanced items; (2) analysis of scale validity and reliability; and (3) analysis of scale dementia diagnostic ability and item importance using machine learning. We then apply SML-AAEA to investigate the effectiveness of our proposed Advanced ADL scale for Early Dementia Screening (AADLs-EDS), which introduces three new advanced items, namely “Going far away”, “Online shopping” and “Using smartphone”. The results show that AADLs-EDS has excellent construct validity, measurement invariance, and scale reliability. The total score of AADLs-EDS can explain the changes in elderly cognitive functions to some extent. The study also finds that AADLs-EDS outperforms the traditional ADL scale in classifying dementia, with the three new items showing the strongest predictive contributions. The findings confirm that AADLs-EDS is a reliable and valid tool for early dementia screening.</p>