Using Machine Learning to Predict Cognitive Impairment Among Middle-Aged and Older Chinese: A Longitudinal Study
Objective: To explore the predictive value of machine learning in cognitive impairment, and identify important factors for cognitive impairment. Methods: A total of 2,326 middle-aged and elderly people completed questionnaire, and physical examination evaluation at baseline, Year 2, and Year 4 follo...
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| Vydáno v: | International journal of public health Ročník 68; s. 1605322 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Switzerland
Frontiers Media S.A
19.01.2023
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| ISSN: | 1661-8564, 1661-8556, 1661-8564 |
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| Abstract | Objective:
To explore the predictive value of machine learning in cognitive impairment, and identify important factors for cognitive impairment.
Methods:
A total of 2,326 middle-aged and elderly people completed questionnaire, and physical examination evaluation at baseline, Year 2, and Year 4 follow-ups. A random forest machine learning (ML) model was used to predict the cognitive impairment at Year 2 and Year 4 longitudinally. Based on Year 4 cross-sectional data, the same method was applied to establish a prediction model and verify its longitudinal prediction accuracy for cognitive impairment. Meanwhile, the ability of random forest and traditional logistic regression model to longitudinally predict 2-year and 4-year cognitive impairment was compared.
Results:
Random forest models showed high accuracy for all outcomes at Year 2, Year 4, and cross-sectional Year 4 [AUC = 0.81, 0.79, 0.80] compared with logistic regression [AUC = 0.61, 0.62, 0.70]. Baseline physical examination (e.g., BMI, Blood pressure), biomarkers (e.g., cholesterol), functioning (e.g., functional limitations), demography (e.g., age), and emotional status (e.g., depression) characteristics were identified as the top ten important predictors of cognitive impairment.
Conclusion:
ML algorithms could enhance the prediction of cognitive impairment among the middle-aged and older Chinese for 4 years and identify essential risk markers. |
|---|---|
| AbstractList | Objective: To explore the predictive value of machine learning in cognitive impairment, and identify important factors for cognitive impairment.Methods: A total of 2,326 middle-aged and elderly people completed questionnaire, and physical examination evaluation at baseline, Year 2, and Year 4 follow-ups. A random forest machine learning (ML) model was used to predict the cognitive impairment at Year 2 and Year 4 longitudinally. Based on Year 4 cross-sectional data, the same method was applied to establish a prediction model and verify its longitudinal prediction accuracy for cognitive impairment. Meanwhile, the ability of random forest and traditional logistic regression model to longitudinally predict 2-year and 4-year cognitive impairment was compared.Results: Random forest models showed high accuracy for all outcomes at Year 2, Year 4, and cross-sectional Year 4 [AUC = 0.81, 0.79, 0.80] compared with logistic regression [AUC = 0.61, 0.62, 0.70]. Baseline physical examination (e.g., BMI, Blood pressure), biomarkers (e.g., cholesterol), functioning (e.g., functional limitations), demography (e.g., age), and emotional status (e.g., depression) characteristics were identified as the top ten important predictors of cognitive impairment.Conclusion: ML algorithms could enhance the prediction of cognitive impairment among the middle-aged and older Chinese for 4 years and identify essential risk markers. Objective: To explore the predictive value of machine learning in cognitive impairment, and identify important factors for cognitive impairment. Methods: A total of 2,326 middle-aged and elderly people completed questionnaire, and physical examination evaluation at baseline, Year 2, and Year 4 follow-ups. A random forest machine learning (ML) model was used to predict the cognitive impairment at Year 2 and Year 4 longitudinally. Based on Year 4 cross-sectional data, the same method was applied to establish a prediction model and verify its longitudinal prediction accuracy for cognitive impairment. Meanwhile, the ability of random forest and traditional logistic regression model to longitudinally predict 2-year and 4-year cognitive impairment was compared. Results: Random forest models showed high accuracy for all outcomes at Year 2, Year 4, and cross-sectional Year 4 [AUC = 0.81, 0.79, 0.80] compared with logistic regression [AUC = 0.61, 0.62, 0.70]. Baseline physical examination (e.g., BMI, Blood pressure), biomarkers (e.g., cholesterol), functioning (e.g., functional limitations), demography (e.g., age), and emotional status (e.g., depression) characteristics were identified as the top ten important predictors of cognitive impairment. Conclusion: ML algorithms could enhance the prediction of cognitive impairment among the middle-aged and older Chinese for 4 years and identify essential risk markers.Objective: To explore the predictive value of machine learning in cognitive impairment, and identify important factors for cognitive impairment. Methods: A total of 2,326 middle-aged and elderly people completed questionnaire, and physical examination evaluation at baseline, Year 2, and Year 4 follow-ups. A random forest machine learning (ML) model was used to predict the cognitive impairment at Year 2 and Year 4 longitudinally. Based on Year 4 cross-sectional data, the same method was applied to establish a prediction model and verify its longitudinal prediction accuracy for cognitive impairment. Meanwhile, the ability of random forest and traditional logistic regression model to longitudinally predict 2-year and 4-year cognitive impairment was compared. Results: Random forest models showed high accuracy for all outcomes at Year 2, Year 4, and cross-sectional Year 4 [AUC = 0.81, 0.79, 0.80] compared with logistic regression [AUC = 0.61, 0.62, 0.70]. Baseline physical examination (e.g., BMI, Blood pressure), biomarkers (e.g., cholesterol), functioning (e.g., functional limitations), demography (e.g., age), and emotional status (e.g., depression) characteristics were identified as the top ten important predictors of cognitive impairment. Conclusion: ML algorithms could enhance the prediction of cognitive impairment among the middle-aged and older Chinese for 4 years and identify essential risk markers. Objective: To explore the predictive value of machine learning in cognitive impairment, and identify important factors for cognitive impairment. Methods: A total of 2,326 middle-aged and elderly people completed questionnaire, and physical examination evaluation at baseline, Year 2, and Year 4 follow-ups. A random forest machine learning (ML) model was used to predict the cognitive impairment at Year 2 and Year 4 longitudinally. Based on Year 4 cross-sectional data, the same method was applied to establish a prediction model and verify its longitudinal prediction accuracy for cognitive impairment. Meanwhile, the ability of random forest and traditional logistic regression model to longitudinally predict 2-year and 4-year cognitive impairment was compared. Results: Random forest models showed high accuracy for all outcomes at Year 2, Year 4, and cross-sectional Year 4 [AUC = 0.81, 0.79, 0.80] compared with logistic regression [AUC = 0.61, 0.62, 0.70]. Baseline physical examination (e.g., BMI, Blood pressure), biomarkers (e.g., cholesterol), functioning (e.g., functional limitations), demography (e.g., age), and emotional status (e.g., depression) characteristics were identified as the top ten important predictors of cognitive impairment. Conclusion: ML algorithms could enhance the prediction of cognitive impairment among the middle-aged and older Chinese for 4 years and identify essential risk markers. Objective: To explore the predictive value of machine learning in cognitive impairment, and identify important factors for cognitive impairment. Methods: A total of 2,326 middle-aged and elderly people completed questionnaire, and physical examination evaluation at baseline, Year 2, and Year 4 follow-ups. A random forest machine learning (ML) model was used to predict the cognitive impairment at Year 2 and Year 4 longitudinally. Based on Year 4 cross-sectional data, the same method was applied to establish a prediction model and verify its longitudinal prediction accuracy for cognitive impairment. Meanwhile, the ability of random forest and traditional logistic regression model to longitudinally predict 2-year and 4-year cognitive impairment was compared. Results: Random forest models showed high accuracy for all outcomes at Year 2, Year 4, and cross-sectional Year 4 [AUC = 0.81, 0.79, 0.80] compared with logistic regression [AUC = 0.61, 0.62, 0.70]. Baseline physical examination (e.g., BMI, Blood pressure), biomarkers (e.g., cholesterol), functioning (e.g., functional limitations), demography (e.g., age), and emotional status (e.g., depression) characteristics were identified as the top ten important predictors of cognitive impairment. Conclusion: ML algorithms could enhance the prediction of cognitive impairment among the middle-aged and older Chinese for 4 years and identify essential risk markers. To explore the predictive value of machine learning in cognitive impairment, and identify important factors for cognitive impairment. A total of 2,326 middle-aged and elderly people completed questionnaire, and physical examination evaluation at baseline, Year 2, and Year 4 follow-ups. A random forest machine learning (ML) model was used to predict the cognitive impairment at Year 2 and Year 4 longitudinally. Based on Year 4 cross-sectional data, the same method was applied to establish a prediction model and verify its longitudinal prediction accuracy for cognitive impairment. Meanwhile, the ability of random forest and traditional logistic regression model to longitudinally predict 2-year and 4-year cognitive impairment was compared. Random forest models showed high accuracy for all outcomes at Year 2, Year 4, and cross-sectional Year 4 [AUC = 0.81, 0.79, 0.80] compared with logistic regression [AUC = 0.61, 0.62, 0.70]. Baseline physical examination (e.g., BMI, Blood pressure), biomarkers (e.g., cholesterol), functioning (e.g., functional limitations), demography (e.g., age), and emotional status (e.g., depression) characteristics were identified as the top ten important predictors of cognitive impairment. ML algorithms could enhance the prediction of cognitive impairment among the middle-aged and older Chinese for 4 years and identify essential risk markers. |
| Author | Zhang, Xiaolei Chong, Sheau Tsuey Liu, Haining Liu, Haihong |
| AuthorAffiliation | 5 Hebei Key Laboratory of Nerve Injury and Repair , Chengde Medical University , Chengde , China 4 Faculty of Engineering , Universiti Putra Malaysia , Serdang , Malaysia 7 Counselling Psychology Programme , Secretariat of Postgraduate Studies , Faculty of Social Sciences and Humanities , Universiti Kebangsaan Malaysia , Bangi , Malaysia 1 Centre for Research in Psychology and Human Well-being, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia , Bangi , Malaysia 2 Department of Psychology , Chengde Medical University , Chengde , China 3 Department of Biomedical Engineering , Chengde Medical University , Chengde , China 6 Hebei International Research Center of Medical Engineering , Chengde Medical University , Chengde , China |
| AuthorAffiliation_xml | – name: 6 Hebei International Research Center of Medical Engineering , Chengde Medical University , Chengde , China – name: 3 Department of Biomedical Engineering , Chengde Medical University , Chengde , China – name: 7 Counselling Psychology Programme , Secretariat of Postgraduate Studies , Faculty of Social Sciences and Humanities , Universiti Kebangsaan Malaysia , Bangi , Malaysia – name: 4 Faculty of Engineering , Universiti Putra Malaysia , Serdang , Malaysia – name: 2 Department of Psychology , Chengde Medical University , Chengde , China – name: 1 Centre for Research in Psychology and Human Well-being, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia , Bangi , Malaysia – name: 5 Hebei Key Laboratory of Nerve Injury and Repair , Chengde Medical University , Chengde , China |
| Author_xml | – sequence: 1 givenname: Haihong surname: Liu fullname: Liu, Haihong – sequence: 2 givenname: Xiaolei surname: Zhang fullname: Zhang, Xiaolei – sequence: 3 givenname: Haining surname: Liu fullname: Liu, Haining – sequence: 4 givenname: Sheau Tsuey surname: Chong fullname: Chong, Sheau Tsuey |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36798738$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1016_j_ypmed_2025_108307 crossref_primary_10_3389_fcvm_2025_1477185 crossref_primary_10_3389_fendo_2024_1290286 crossref_primary_10_1186_s12889_024_18692_7 crossref_primary_10_1002_hsr2_1635 crossref_primary_10_3390_healthcare12101028 crossref_primary_10_2196_54335 crossref_primary_10_1186_s12877_023_04477_x crossref_primary_10_3390_brainsci15050446 crossref_primary_10_1186_s12889_025_23310_1 crossref_primary_10_3389_fnagi_2023_1283243 |
| Cites_doi | 10.1186/1471-2156-5-32 10.1016/j.maturitas.2021.03.011 10.1016/j.archger.2020.104034 10.1186/s13024-017-0167-y 10.1093/gerona/glx175 10.5551/jat.52928 10.1161/HYPERTENSIONAHA.108.126342 10.1016/j.neuroscience.2021.07.022 10.1053/apnr.2000.9231 10.2147/NDT.S145812 10.1212/WNL.0b013e3182190d09 10.1023/a:1010933404324 10.1159/000089515 10.1016/j.psychres.2011.11.012 10.2174/1871527319666200611130804 10.1016/j.jagp.2021.05.014 10.1016/j.arr.2019.101001 10.1016/j.jsmc.2017.09.009 10.1001/archneurol.2009.308 10.1111/jgs.14835 10.3390/nu12092644 10.1161/JAHA.119.014621 10.1038/s41598-020-77296-4 10.1080/15376516.2020.1760984 10.18295/squmj.2016.16.01.009 10.1097/JGP.0b013e31819431d5 10.1212/01.wnl.0000251303.50459.8a 10.3390/nu13062118 10.1016/j.smrv.2019.101250 10.1097/MD.0000000000018248 10.1038/mp.2015.198 10.1002/gps.2503 10.1038/s41598-019-39478-7 10.1017/S0033291720000227 10.1016/j.jad.2018.11.073 10.1370/afm.2367 10.3389/fpsyg.2018.00371 10.14569/ijacsa.2015.061202 10.1186/1756-0500-4-299 10.1016/j.atherosclerosis.2020.10.002 10.1016/j.jad.2021.07.106 10.1016/j.nicl.2014.08.023 10.3390/ijerph19074000 10.1016/j.jbi.2015.05.016 10.1093/geronb/gby128 10.1016/j.sjbs.2021.06.023 10.3233/JAD-2012-120835 10.2165/00019053-200725120-00003 10.3233/jad-201438 10.1093/ije/dys203 10.1007/978-0-387-79948-3_1077 10.1038/s41598-020-75767-2 10.1007/s11357-019-00070-6 10.18632/aging.104078 10.4172/2161-0460.1000435 10.1212/WNL.0b013e3182a08f1b 10.1037/ort0000018 10.1177/23337214211025167 10.1016/j.neurobiolaging.2008.01.008 10.1038/s41386-020-0767-z 10.1007/s10916-018-1088-1 10.1016/j.jstrokecerebrovasdis.2020.105334 10.1111/j.1749-6632.2010.05726.x 10.1186/s12859-018-2264-5 10.6890/IJGE.201909_13(3).0005 10.1097/MD.0000000000014736 10.1016/j.psychres.2021.113823 10.1371/journal.pone.0244773 10.1371/journal.pone.0082450 10.1111/jcpp.12916 10.1186/s13195-019-0527-7 10.1037/neu0000335 10.3233/JAD-190967 10.3390/ijerph16122168 10.1016/s0140-6736(20)30367-6 10.1016/j.jad.2021.08.093 10.1093/arclin/acx021 10.3233/jad-170865 |
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| Keywords | middle-aged and older Chinese dementia cognitive impairment longitudinal study random forest machine learning |
| Language | English |
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| References | Korthauer (B82) 2018; 73 Li (B20) 2014; 9 Seeman (B38) 2004; 30 Roe (B51) 2007; 68 Yang (B47) 2019; 98 Futoma (B10) 2015; 56 Crimmins (B37) 2001 Pondugula (B64) 2020; 30 Nagaraj (B43) 2021; 80 Noh (B65) 2019; 98 Na (B22) 2019; 9 Costanza (B31) 2015; 11 Johansona (B55) 2018; 8 Byeon (B16) 2015; 6 Dzierzewski (B85) 2018; 13 Roebuck-Spencer (B77) 2017; 32 Troyer (B36) 2011 Xu (B66) 2011; 76 Lunetta (B14) 2004; 5 de Montgolfier (B71) 2019; 41 Perez (B25) 2021; 29 Farfel (B50) 2013; 81 Adam (B83) 2016; 16 Noble (B57) 2010; 67 Dimache (B63) 2021; 13 Streit (B70) 2019; 17 Duggan (B74) 2020; 75 Ma (B60) 2017; 12 Elias (B72) 2009; 53 Chen (B34) 2020; 12 Dos Santos (B81) 2018; 9 Kessler (B11) 2016; 21 Farias (B76) 2017; 65 Maroco (B45) 2011; 4 Laurin (B59) 2009; 30 Vanoh (B49) 2019; 13 Abd-El Mohsen (B23) 2021; 28 Ren (B26) 2021; 295 Zhao (B33) 2014; 43 Haynos (B42) 2020; 51 Gomez-Ramirez (B21) 2020; 10 An (B44) 2020; 10 Walsh (B6) 2018; 59 Jak (B40) 2009; 17 Katayama (B28) 2021; 94 Lu (B32) 2021; 472 Pangman (B35) 2000; 13 Lee (B75) 2019; 16 Breiman (B12) 2001; 45 Livingston (B5) 2020; 396 Gorelick (B56) 2010; 1207 Lebedev (B19) 2014; 6 Xia (B69) 2020; 27 Avgerinos (B62) 2020; 58 Liew (B79) 2019; 11 Espeland (B4) 2011; 26 Šimundić (B41) 2009; 19 Patnode (B3) 2020 Burke (B7) 2019; 245 Chen (B27) 2021; 148 Alvarez-Bueno (B73) 2020; 9 Md Fadzil (B78) 2022; 19 Costanza (B30) 2020; 19 van Eeden (B8) 2021; 299 Costanza (B24) 2012; 32 Prince (B1) 2013 Koppe (B9) 2021; 46 Aschwanden (B67) 2020; 75 Sattler (B48) 2012; 196 Anwar (B18) 2018; 42 Zlatar (B80) 2018; 63 Parthasarathy (B61) 2017; 31 Fathima (B13) 2015; 6 Kuroki (B15) 2015; 85 Jimenez-Balado (B53) 2020; 312 Mohd Safien (B84) 2021; 7 Prince (B2) 2015 Getsios (B52) 2007; 25 Bubu (B86) 2020; 50 Fang (B29) 2021; 294 Xue (B54) 2017; 13 Williams (B39) 2020; 29 Velazquez (B17) 2021; 16 Couronne (B46) 2018; 19 Ravaglia (B68) 2006; 21 Ooi (B58) 2020; 12 37273772 - Int J Public Health. 2023 May 18;68:1606127 |
| References_xml | – volume: 5 start-page: 32 year: 2004 ident: B14 article-title: Screening Large-Scale Association Study Data: Exploiting Interactions Using Random Forests publication-title: BMC Genet doi: 10.1186/1471-2156-5-32 – volume: 148 start-page: 7 year: 2021 ident: B27 article-title: Leisure Activities and Psychological Wellbeing Reduce the Risk of Cognitive Impairment Among Older Adults with Hearing Difficulty: A Longitudinal Study in china publication-title: Maturitas doi: 10.1016/j.maturitas.2021.03.011 – volume: 94 start-page: 104034 year: 2021 ident: B28 article-title: Relationship between Instrumental Activities of Daily Living Performance and Incidence of Mild Cognitive Impairment Among Older Adults: A 48-month Follow-Up Study publication-title: Arch Gerontol Geriatr doi: 10.1016/j.archger.2020.104034 – volume: 12 start-page: 24 year: 2017 ident: B60 article-title: Blood Cholesterol in Late-Life and Cognitive Decline: A Longitudinal Study of the Chinese Elderly publication-title: Mol Neurodegener doi: 10.1186/s13024-017-0167-y – volume: 73 start-page: 506 year: 2018 ident: B82 article-title: Negative Affect Is Associated with Higher Risk of Incident Cognitive Impairment in Nondepressed Postmenopausal Women publication-title: J Gerontol A Biol Sci Med Sci doi: 10.1093/gerona/glx175 – volume: 27 start-page: 934 year: 2020 ident: B69 article-title: The Relationship of Coronary Artery Calcium and Clinical Coronary Artery Disease with Cognitive Function: A Systematic Review and Meta-Analysis publication-title: J Atheroscler Thromb doi: 10.5551/jat.52928 – volume: 30 start-page: 89 year: 2004 ident: B38 article-title: Supplement: Aging, Health, and Public Policy || Integrating Biology into the Study of Health Disparities publication-title: Popul Develop Rev – volume: 53 start-page: 668 year: 2009 ident: B72 article-title: Arterial Pulse Wave Velocity and Cognition with Advancing Age publication-title: Hypertension doi: 10.1161/HYPERTENSIONAHA.108.126342 – volume: 472 start-page: 25 year: 2021 ident: B32 article-title: Nonlinear Phase Synchronization Analysis of Eeg Signals in Amnesic Mild Cognitive Impairment with Type 2 Diabetes Mellitus publication-title: Neuroscience doi: 10.1016/j.neuroscience.2021.07.022 – volume: 13 start-page: 209 year: 2000 ident: B35 article-title: An Examination of Psychometric Properties of the Mini-Mental State Examination and the Standardized Mini-Mental State Examination: Implications for Clinical Practice publication-title: Appl Nurs Res doi: 10.1053/apnr.2000.9231 – volume: 13 start-page: 2363 year: 2017 ident: B54 article-title: Low Uric Acid Is a Risk Factor in Mild Cognitive Impairment publication-title: Neuropsychiatr Dis Treat doi: 10.2147/NDT.S145812 – volume: 76 start-page: 1568 year: 2011 ident: B66 article-title: Midlife Overweight and Obesity Increase Late-Life Dementia Risk: A Population-Based Twin Study publication-title: Neurology doi: 10.1212/WNL.0b013e3182190d09 – volume: 45 start-page: 5 year: 2001 ident: B12 article-title: Random Forests publication-title: Machine Learn doi: 10.1023/a:1010933404324 – volume: 21 start-page: 51 year: 2006 ident: B68 article-title: Conversion of Mild Cognitive Impairment to Dementia: Predictive Role of Mild Cognitive Impairment Subtypes and Vascular Risk Factors publication-title: Dement Geriatr Cogn Disord doi: 10.1159/000089515 – volume: 196 start-page: 90 year: 2012 ident: B48 article-title: Cognitive Activity, Education and Socioeconomic Status as Preventive Factors for Mild Cognitive Impairment and Alzheimer's Disease publication-title: Psychiatry Res doi: 10.1016/j.psychres.2011.11.012 – volume: 19 start-page: 257 year: 2020 ident: B30 article-title: When Sick Brain and Hopelessness Meet: Some Aspects of Suicidality in the Neurological Patient publication-title: CNS Neurol Disord Drug Targets doi: 10.2174/1871527319666200611130804 – volume: 29 start-page: 1062 year: 2021 ident: B25 article-title: Cognitive Impairment in Older Incarcerated Males: Education and Race Considerations publication-title: Am J Geriatr Psychiatry doi: 10.1016/j.jagp.2021.05.014 – volume: 58 start-page: 101001 year: 2020 ident: B62 article-title: Medium Chain Triglycerides Induce Mild Ketosis and May Improve Cognition in Alzheimer's Disease. A Systematic Review and Meta-Analysis of Human Studies publication-title: Ageing Res Rev doi: 10.1016/j.arr.2019.101001 – volume: 13 start-page: 93 year: 2018 ident: B85 article-title: Sleep and Cognition in Older Adults publication-title: Sleep Med Clin doi: 10.1016/j.jsmc.2017.09.009 – volume: 19 start-page: 203 year: 2009 ident: B41 article-title: Measures of Diagnostic Accuracy: Basic Definitions publication-title: EJIFCC – volume: 67 start-page: 87 year: 2010 ident: B57 article-title: Association of C-Reactive Protein with Cognitive Impairment publication-title: Arch Neurol doi: 10.1001/archneurol.2009.308 – volume: 65 start-page: 1152 year: 2017 ident: B76 article-title: Early Functional Limitations in Cognitively normal Older Adults Predict Diagnostic Conversion to Mild Cognitive Impairment publication-title: J Am Geriatr Soc doi: 10.1111/jgs.14835 – volume: 12 start-page: 2644 year: 2020 ident: B58 article-title: Intermittent Fasting Enhanced the Cognitive Function in Older Adults with Mild Cognitive Impairment by Inducing Biochemical and Metabolic Changes: A 3-year Progressive Study publication-title: Nutrients doi: 10.3390/nu12092644 – volume: 9 start-page: e014621 year: 2020 ident: B73 article-title: Arterial Stiffness and Cognition Among Adults: A Systematic Review and Meta-Analysis of Observational and Longitudinal Studies publication-title: J Am Heart Assoc doi: 10.1161/JAHA.119.014621 – volume: 10 start-page: 20630 year: 2020 ident: B21 article-title: Selecting the Most Important Self-Assessed Features for Predicting Conversion to Mild Cognitive Impairment with Random forest and Permutation-Based Methods publication-title: Sci Rep doi: 10.1038/s41598-020-77296-4 – volume: 11 start-page: 402 year: 2015 ident: B31 article-title: Neurological Diseases and Suicide: From Neurobiology to Hopelessness publication-title: Rev Med Suisse – start-page: 14 volume-title: Policy Brief for Heads of Government: The Global Impact of Dementia 2013-2050 year: 2013 ident: B1 – volume: 30 start-page: 454 year: 2020 ident: B64 article-title: Predictable Hematological Markers Associated with Cognitive Decline in Valid Rodent Models of Cognitive Impairment publication-title: Toxicol Mech Methods doi: 10.1080/15376516.2020.1760984 – volume: 16 start-page: e47 year: 2016 ident: B83 article-title: Effectiveness of a Combined Dance and Relaxation Intervention on Reducing Anxiety and Depression and Improving Quality of Life Among the Cognitively Impaired Elderly publication-title: Sultan Qaboos Univ Med J doi: 10.18295/squmj.2016.16.01.009 – volume: 17 start-page: 368 year: 2009 ident: B40 article-title: Quantification of Five Neuropsychological Approaches to Defining Mild Cognitive Impairment publication-title: Am J Geriatr Psychiatry doi: 10.1097/JGP.0b013e31819431d5 – volume: 68 start-page: 223 year: 2007 ident: B51 article-title: Education and Alzheimer Disease without Dementia: Support for the Cognitive reserve Hypothesis publication-title: Neurology doi: 10.1212/01.wnl.0000251303.50459.8a – volume: 13 start-page: 2118 year: 2021 ident: B63 article-title: The Role of High Triglycerides Level in Predicting Cognitive Impairment: A Review of Current Evidence publication-title: Nutrients doi: 10.3390/nu13062118 – volume: 50 start-page: 101250 year: 2020 ident: B86 article-title: Obstructive Sleep Apnea, Cognition and Alzheimer's Disease: A Systematic Review Integrating Three Decades of Multidisciplinary Research publication-title: Sleep Med Rev doi: 10.1016/j.smrv.2019.101250 – volume: 98 start-page: e18248 year: 2019 ident: B47 article-title: Comparison of Prevalence and Associated Risk Factors of Cognitive Function Status Among Elderly between Nursing Homes and Common Communities of china: A Strobe-Compliant Observational Study publication-title: Medicine doi: 10.1097/MD.0000000000018248 – volume: 21 start-page: 1366 year: 2016 ident: B11 article-title: Testing a Machine-Learning Algorithm to Predict the Persistence and Severity of Major Depressive Disorder from Baseline Self-Reports publication-title: Mol Psychiatry doi: 10.1038/mp.2015.198 – volume: 26 start-page: 135 year: 2011 ident: B4 article-title: Telephone Interview for Cognitive Status (TICS) Screening for Clinical Trials of Physical Activity and Cognitive Training: The Seniors Health and Activity Research Program Pilot (SHARP-P) Study publication-title: Int J Geriatr Psychiatry doi: 10.1002/gps.2503 – volume: 9 start-page: 3335 year: 2019 ident: B22 article-title: Prediction of Future Cognitive Impairment Among the Community Elderly: A Machine-Learning Based Approach publication-title: Sci Rep doi: 10.1038/s41598-019-39478-7 – volume: 51 start-page: 1392 year: 2020 ident: B42 article-title: Machine Learning Enhances Prediction of Illness Course: A Longitudinal Study in Eating Disorders publication-title: Psychol Med doi: 10.1017/S0033291720000227 – volume: 245 start-page: 869 year: 2019 ident: B7 article-title: The Use of Machine Learning in the Study of Suicidal and Non-suicidal Self-Injurious Thoughts and Behaviors: A Systematic Review publication-title: J Affect Disord doi: 10.1016/j.jad.2018.11.073 – volume: 17 start-page: 100 year: 2019 ident: B70 article-title: Systolic Blood Pressure and Cognitive Decline in Older Adults with Hypertension publication-title: Ann Fam Med doi: 10.1370/afm.2367 – volume: 9 start-page: 371 year: 2018 ident: B81 article-title: Association of Lower Spiritual Well-Being, Social Support, Self-Esteem, Subjective Well-Being, Optimism and hope Scores with Mild Cognitive Impairment and Mild Dementia publication-title: Front Psychol doi: 10.3389/fpsyg.2018.00371 – volume: 6 start-page: 240 year: 2015 ident: B13 article-title: Analysis of Significant Factors for Dengue Infection Prognosis Using the Random forest Classifier publication-title: Int J Adv Comput Sci Appl – volume: 6 start-page: 8 year: 2015 ident: B16 article-title: A Prediction Model for Mild Cognitive Impairment Using Random Forests publication-title: J Int J Adv Comput Sci App doi: 10.14569/ijacsa.2015.061202 – volume: 4 start-page: 299 year: 2011 ident: B45 article-title: Data Mining Methods in the Prediction of Dementia: A Real-Data Comparison of the Accuracy, Sensitivity and Specificity of Linear Discriminant Analysis, Logistic Regression, Neural Networks, Support Vector Machines, Classification Trees and Random Forests publication-title: BMC Res Notes doi: 10.1186/1756-0500-4-299 – volume: 312 start-page: 101 year: 2020 ident: B53 article-title: Non-invasive Markers of Vascular Disease: An Opportunity for Early Diagnosis of Cognitive Impairment publication-title: Atherosclerosis doi: 10.1016/j.atherosclerosis.2020.10.002 – volume: 294 start-page: 847 year: 2021 ident: B29 article-title: Brain Controllability Distinctiveness between Depression and Cognitive Impairment publication-title: J Affect Disord doi: 10.1016/j.jad.2021.07.106 – volume: 6 start-page: 115 year: 2014 ident: B19 article-title: Random forest Ensembles for Detection and Prediction of Alzheimer's Disease with a Good Between-Cohort Robustness publication-title: Neuroimage Clin doi: 10.1016/j.nicl.2014.08.023 – volume: 19 start-page: 4000 year: 2022 ident: B78 article-title: A Scoping Review for Usage of Telerehabilitation Among Older Adults with Mild Cognitive Impairment or Cognitive Frailty publication-title: Int J Environ Res Public Health doi: 10.3390/ijerph19074000 – volume: 56 start-page: 229 year: 2015 ident: B10 article-title: A Comparison of Models for Predicting Early Hospital Readmissions publication-title: J Biomed Inform doi: 10.1016/j.jbi.2015.05.016 – volume: 75 start-page: 937 year: 2020 ident: B74 article-title: Systematic Review of Pulmonary Function and Cognition in Aging publication-title: J Gerontol B Psychol Sci Soc Sci doi: 10.1093/geronb/gby128 – volume: 28 start-page: 5781 year: 2021 ident: B23 article-title: Predicting Cognitive Impairment Among Geriatric Patients at Asir central Hospital, saudi arabia publication-title: Saudi J Biol Sci doi: 10.1016/j.sjbs.2021.06.023 – volume: 32 start-page: 643 year: 2012 ident: B24 article-title: Microvascular burden and Alzheimer-type Lesions across the Age Spectrum publication-title: J Alzheimers Dis doi: 10.3233/JAD-2012-120835 – volume: 25 start-page: 997 year: 2007 ident: B52 article-title: Nice Cost-Effectiveness Appraisal of Cholinesterase Inhibitors publication-title: Pharmacoeconomics doi: 10.2165/00019053-200725120-00003 – volume: 80 start-page: 1079 year: 2021 ident: B43 article-title: Deep Learning and Risk Score Classification of Mild Cognitive Impairment and Alzheimer’s Disease publication-title: J Alzheimer's Dis doi: 10.3233/jad-201438 – volume: 43 start-page: 61 year: 2014 ident: B33 article-title: Cohort Profile: The china Health and Retirement Longitudinal Study (Charls) publication-title: Int J Epidemiol doi: 10.1093/ije/dys203 – start-page: 28 volume-title: Encyclopedia of Clinical Neuropsychology year: 2011 ident: B36 article-title: Activities of Daily Living (ADL) doi: 10.1007/978-0-387-79948-3_1077 – volume: 10 start-page: 18716 year: 2020 ident: B44 article-title: Machine Learning Prediction for Mortality of Patients Diagnosed with Covid-19: A Nationwide Korean Cohort Study publication-title: Sci Rep doi: 10.1038/s41598-020-75767-2 – volume: 41 start-page: 511 year: 2019 ident: B71 article-title: Systolic Hypertension-Induced Neurovascular Unit Disruption Magnifies Vascular Cognitive Impairment in Middle-Age Atherosclerotic ldlr(-/-):Hapob(+/+) Mice publication-title: Geroscience doi: 10.1007/s11357-019-00070-6 – volume-title: U.S. Preventive Services Task Force Evidence Syntheses, Formerly Systematic Evidence Reviews year: 2020 ident: B3 – volume: 12 start-page: 23129 year: 2020 ident: B34 article-title: Cognitive Frailty in Relation to Adverse Health Outcomes Independent of Multimorbidity: Results from the china Health and Retirement Longitudinal Study publication-title: Aging doi: 10.18632/aging.104078 – volume: 8 start-page: 435 year: 2018 ident: B55 article-title: Disrupted Blood-Csf Barrier to Urea and Creatinine in Mild Cognitive Impairment and Alzheimer's Disease publication-title: J Alzheimer's Dis doi: 10.4172/2161-0460.1000435 – volume: 81 start-page: 650 year: 2013 ident: B50 article-title: Very Low Levels of Education and Cognitive reserve: A Clinicopathologic Study publication-title: Neurology doi: 10.1212/WNL.0b013e3182a08f1b – volume: 85 start-page: 34 year: 2015 ident: B15 article-title: Risk Factors for Suicidal Behaviors Among Filipino Americans: A Data Mining Approach publication-title: Am J Orthopsychiatry doi: 10.1037/ort0000018 – volume: 7 start-page: 23337214211025167 year: 2021 ident: B84 article-title: Randomized Controlled Trials of a Psychosocial Intervention for Improving the Cognitive Function Among Older Adults: A Scoping Review publication-title: Gerontol Geriatr Med doi: 10.1177/23337214211025167 – volume: 30 start-page: 1724 year: 2009 ident: B59 article-title: Midlife C-Reactive Protein and Risk of Cognitive Decline: A 31-year Follow-Up publication-title: Neurobiol Aging doi: 10.1016/j.neurobiolaging.2008.01.008 – volume: 46 start-page: 176 year: 2021 ident: B9 article-title: Deep Learning for Small and Big Data in Psychiatry publication-title: Neuropsychopharmacology doi: 10.1038/s41386-020-0767-z – volume: 42 start-page: 226 year: 2018 ident: B18 article-title: Medical Image Analysis Using Convolutional Neural Networks: A Review publication-title: J Med Syst doi: 10.1007/s10916-018-1088-1 – volume: 29 start-page: 105334 year: 2020 ident: B39 article-title: Validation of the 10-item center for Epidemiologic Studies Depression Scale post Stroke publication-title: J Stroke Cerebrovasc Dis doi: 10.1016/j.jstrokecerebrovasdis.2020.105334 – volume: 1207 start-page: 155 year: 2010 ident: B56 article-title: Role of Inflammation in Cognitive Impairment: Results of Observational Epidemiological Studies and Clinical Trials publication-title: Ann N Y Acad Sci doi: 10.1111/j.1749-6632.2010.05726.x – volume: 19 start-page: 270 year: 2018 ident: B46 article-title: Random forest versus Logistic Regression: A Large-Scale Benchmark experiment publication-title: BMC Bioinformatics doi: 10.1186/s12859-018-2264-5 – volume: 13 start-page: 207 year: 2019 ident: B49 article-title: Influence of Gender Disparity in Predicting Occurrence of Successful Aging, Usual Aging and Mild Cognitive Impairment publication-title: J Int J Gerontol doi: 10.6890/IJGE.201909_13(3).0005 – volume: 98 start-page: e14736 year: 2019 ident: B65 article-title: Sex Differences in the Relationship between Cognitive Impairment and Overweight or Obesity in Late Life: A 3-year Prospective Study publication-title: Medicine doi: 10.1097/MD.0000000000014736 – volume: 299 start-page: 113823 year: 2021 ident: B8 article-title: Predicting the 9-year Course of Mood and Anxiety Disorders with Automated Machine Learning: A Comparison between Auto-Sklearn, Naïve Bayes Classifier, and Traditional Logistic Regression publication-title: Psychiatry Res doi: 10.1016/j.psychres.2021.113823 – volume: 16 start-page: e0244773 year: 2021 ident: B17 article-title: Random forest Model for Feature-Based Alzheimer's Disease Conversion Prediction from Early Mild Cognitive Impairment Subjects publication-title: PLoS One doi: 10.1371/journal.pone.0244773 – volume: 9 start-page: e82450 year: 2014 ident: B20 article-title: Hierarchical Interactions Model for Predicting Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD) Conversion publication-title: PLoS One doi: 10.1371/journal.pone.0082450 – volume-title: Integrating Biology into Demographic Research on Health and Aging (With a Focus on the Macarthur Study of Successful Aging). Cells and Surveys: Should Biological Measures Be Included in Social Science Research? year: 2001 ident: B37 – volume: 59 start-page: 1261 year: 2018 ident: B6 article-title: Predicting Suicide Attempts in Adolescents with Longitudinal Clinical Data and Machine Learning publication-title: J Child Psychol Psychiatry doi: 10.1111/jcpp.12916 – start-page: 89 volume-title: World Alzheimer Report 2015 - the Global Impact of Dementia year: 2015 ident: B2 – volume: 11 start-page: 70 year: 2019 ident: B79 article-title: Depression, Subjective Cognitive Decline, and the Risk of Neurocognitive Disorders publication-title: Alzheimers Res Ther doi: 10.1186/s13195-019-0527-7 – volume: 31 start-page: 682 year: 2017 ident: B61 article-title: Triglycerides Are Negatively Correlated with Cognitive Function in Nondemented Aging Adults publication-title: Neuropsychology doi: 10.1037/neu0000335 – volume: 75 start-page: 717 year: 2020 ident: B67 article-title: Predicting Cognitive Impairment and Dementia: A Machine Learning Approach publication-title: J Alzheimers Dis doi: 10.3233/JAD-190967 – volume: 16 start-page: 2168 year: 2019 ident: B75 article-title: Association between Ambulatory Status and Functional Disability in Elderly People with Dementia publication-title: Int J Environ Res Public Health doi: 10.3390/ijerph16122168 – volume: 396 start-page: 413 year: 2020 ident: B5 article-title: Dementia Prevention, Intervention, and Care: 2020 Report of the Lancet Commission publication-title: The Lancet doi: 10.1016/s0140-6736(20)30367-6 – volume: 295 start-page: 463 year: 2021 ident: B26 article-title: Associations of Body Mass index, Waist Circumference and Waist-To-Height Ratio with Cognitive Impairment Among Chinese Older Adults: Based on the Clhls publication-title: J Affect Disord doi: 10.1016/j.jad.2021.08.093 – volume: 32 start-page: 491 year: 2017 ident: B77 article-title: Cognitive Screening Tests versus Comprehensive Neuropsychological Test Batteries: A National Academy of Neuropsychology Education Paper† publication-title: Arch Clin Neuropsychol doi: 10.1093/arclin/acx021 – volume: 63 start-page: 949 year: 2018 ident: B80 article-title: Subjective Cognitive Decline, Objective Cognition, and Depression in Older Hispanics Screened for Memory Impairment publication-title: J Alzheimers Dis doi: 10.3233/jad-170865 – reference: 37273772 - Int J Public Health. 2023 May 18;68:1606127 |
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| Snippet | Objective:
To explore the predictive value of machine learning in cognitive impairment, and identify important factors for cognitive impairment.
Methods:
A... To explore the predictive value of machine learning in cognitive impairment, and identify important factors for cognitive impairment. A total of 2,326... Objective: To explore the predictive value of machine learning in cognitive impairment, and identify important factors for cognitive impairment. Methods: A... Objective: To explore the predictive value of machine learning in cognitive impairment, and identify important factors for cognitive impairment. Methods: A... Objective: To explore the predictive value of machine learning in cognitive impairment, and identify important factors for cognitive impairment.Methods: A... |
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| SubjectTerms | Aged Cognitive Dysfunction - diagnosis Cognitive Dysfunction - epidemiology cognitive impairment Cross-Sectional Studies dementia East Asian People Humans Longitudinal Studies longitudinal study Machine Learning Middle Aged middle-aged and older Chinese Public Health Archive random forest |
| Title | Using Machine Learning to Predict Cognitive Impairment Among Middle-Aged and Older Chinese: A Longitudinal Study |
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