It is time-critical to administer pre-clinical cognitive health screening of patients to detect dementia and differentiate from mild cognitive impairment (MCI), and ensure early intervention, treatment and caregiving plans. Current medical standard and practice for pre-clinical screening follows standardized cognitive questionnaire test and patients' verbal response based scoring. However, such practice misses sensing valuable signals of non-verbal stress response relevant to deciding dementia vs. MCI. To this end, we propose a novel approach, Alz-Sense, that integrates non-invasive passive sensing (during the standard cognitive questionnaire test) with an intelligent algorithm based assessment of dementia vs. MCI. The contributions of our Alz-Sense approach are: (i) innovative non-invasive passive sensing of non-verbal stress response from a custom made Smart Chair Cover; (ii) identifying and quantifying a novel indicator of stress response from chair cover multi-sensors data; and (iii) intelligent integration of stress indicator into a revised scoring mechanism. We deployed the Alz-Sense system for patients study in a hospital clinic, and validated its performance through 50 patients dataset. We compare the ROC (Receiver Operating Characteristic) performance of Alz-Sense approach with widely used standardized SLUMS questionnaire based scoring. We also compare performance in the contextual region of ROC curve relevant to pre-clinical cognitive health screening to show advantages of our approach. Further analysis selects optimal model parameters and compares SLUMS performance using medical clinic recommended threshold.