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Non-randomized Protocols and Methods (ehealth) (723) Digital Biomarkers and Digital Phenotyping (323) mHealth for Data Collection and Research (1262) Machine Learning (2234) Wearable Devices and Sensors (1214) Dementia and Cognitive Decline (745) Aging in Place (404) Sensors at Home and Domotics (84) Frailty Detection, Assessment and Prediction (71)

Published on in Vol 15 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/79490, first published .
Development and Validation of Machine Learning Models for Predicting Early Cognitive Decline Using Home Sensor–Derived Behavioral Data: Sensors in-Home for Elder Wellbeing (SINEW) Cohort Study

Development and Validation of Machine Learning Models for Predicting Early Cognitive Decline Using Home Sensor–Derived Behavioral Data: Sensors in-Home for Elder Wellbeing (SINEW) Cohort Study

Development and Validation of Machine Learning Models for Predicting Early Cognitive Decline Using Home Sensor–Derived Behavioral Data: Sensors in-Home for Elder Wellbeing (SINEW) Cohort Study

Authors of this article:

James Patrick Moon1 Author Orcid Image ;   Khalid Abdul Jabbar2 Author Orcid Image ;   Tony Chin Ian Tay2 Author Orcid Image ;   Laura Tay3 Author Orcid Image ;   Rathi Mahendran4 Author Orcid Image ;   Tze Pin Ng5 Author Orcid Image ;   Shian Ming Tan1 Author Orcid Image ;   Wilbur Zhi Hao Koh6 Author Orcid Image ;   Weng Yan Ying7 Author Orcid Image ;   Ah-Hwee Tan7 Author Orcid Image ;   Tih-Shih Lee8 Author Orcid Image ;   Iris Rawtaer1, 2, 9 Author Orcid Image
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