PMID- 31177223 OWN - NLM STAT- MEDLINE DCOM- 20200916 LR - 20200916 IS - 1875-8908 (Electronic) IS - 1387-2877 (Print) IS - 1387-2877 (Linking) VI - 70 IP - 1 DP - 2019 TI - Episodic-Memory Performance in Machine Learning Modeling for Predicting Cognitive Health Status Classification. PG - 277-286 LID - 10.3233/JAD-190165 [doi] AB - BACKGROUND: Memory dysfunction is characteristic of aging and often attributed to Alzheimer's disease (AD). An easily administered tool for preliminary assessment of memory function and early AD detection would be integral in improving patient management. OBJECTIVE: Our primary aim was to utilize machine learning in determining initial viable models to serve as complementary instruments in demonstrating efficacy of the MemTrax online Continuous Recognition Tasks (M-CRT) test for episodic-memory screening and assessing cognitive impairment. METHODS: We used an existing dataset subset (n = 18,395) of demographic information, general health screening questions (addressing memory, sleep quality, medications, and medical conditions affecting thinking), and test results from a convenience sample of adults who took the M-CRT test. M-CRT performance and participant features were used as independent attributes: true positive/negative, percent responses/correct, response time, age, sex, and recent alcohol consumption. For predictive modeling, we used demographic information and test scores to predict binary classification of the health-related questions (yes/no) and general health status (healthy/unhealthy), based on the screening questions. RESULTS: ANOVA revealed significant differences among HealthQScore groups for response time true positive (p = 0.000) and true positive (p = 0.020), but none for true negative (p = 0.0551). Both % responses and % correct had significant differences (p = 0.026 and p = 0.037, respectively). Logistic regression was generally the top-performing learner with moderately robust prediction performance (AUC) for HealthQScore (0.648-0.680) and selected general health questions (0.713-0.769). CONCLUSION: Our novel application of supervised machine learning and predictive modeling helps to demonstrate and validate cross-sectional utility of MemTrax in assessing early-stage cognitive impairment and general screening for AD. FAU - Bergeron, Michael F AU - Bergeron MF AD - SIVOTEC Analytics, Boca Raton, FL, USA. FAU - Landset, Sara AU - Landset S AD - Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA. FAU - Tarpin-Bernard, Franck AU - Tarpin-Bernard F AD - HAPPYneuron, S.A.S., Lyon, France. FAU - Ashford, Curtis B AU - Ashford CB AD - MemTrax, LLC., Redwood City, CA, USA. FAU - Khoshgoftaar, Taghi M AU - Khoshgoftaar TM AD - Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA. FAU - Ashford, J Wesson AU - Ashford JW AD - War-Related Illness and Injury Study Center, VA Palo Alto Health Care System and Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Palo Alto, CA, USA. LA - eng PT - Journal Article PL - Netherlands TA - J Alzheimers Dis JT - Journal of Alzheimer's disease : JAD JID - 9814863 SB - IM MH - Adult MH - Aged MH - Aged, 80 and over MH - Aging/*psychology MH - Alzheimer Disease/*diagnosis/psychology MH - Cognition/*physiology MH - Cognitive Dysfunction/*diagnosis/psychology MH - Databases, Factual MH - Dementia/*diagnosis/psychology MH - Female MH - Health Status MH - Humans MH - *Machine Learning MH - Male MH - Mass Screening MH - *Memory, Episodic MH - Middle Aged MH - Models, Psychological MH - Neuropsychological Tests PMC - PMC6700609 OTO - NOTNLM OT - Aging OT - Alzheimer's disease OT - dementia OT - mass screening COIS- Authors' disclosures available online (https://www.j-alz.com/manuscript-disclosures/19-0165r1). EDAT- 2019/06/10 06:00 MHDA- 2020/09/17 06:00 PMCR- 2019/08/20 CRDT- 2019/06/10 06:00 PHST- 2019/06/10 06:00 [pubmed] PHST- 2020/09/17 06:00 [medline] PHST- 2019/06/10 06:00 [entrez] PHST- 2019/08/20 00:00 [pmc-release] AID - JAD190165 [pii] AID - 10.3233/JAD-190165 [doi] PST - ppublish SO - J Alzheimers Dis. 2019;70(1):277-286. doi: 10.3233/JAD-190165.