Genetic algorithm with logistic regression for prediction of progression to Alzheimer's disease

BMC Bioinformatics

Volumn 15, Issue , 2014, Pages

  Johnson, Piers ^a   Vandewater, Luke ^a   Wilson, William ^a,j   Maruff, Paul ^b,c   Savage, Greg ^d   Graham, Petra ^d   Macaulay, Lance S ^a   Ellis, Kathryn A ^b,e   Szoeke, Cassandra ^b   Martins, Ralph N ^f,g   Rowe, Christopher C ^e,h   Masters, Colin L ^d,e   Ames, David ^e,i   Zhang, Ping ^a,j  

^a CSIRO   (Australia)

^b FLOREY INSTITUTE OF NEUROSCIENCE AND MENTAL HEALTH   (Australia)

^c Scientific Division   (Australia)

^d MACQUARIE UNIVERSITY   (Australia)

^e UNIVERSITY OF MELBOURNE   (Australia)

^f EDITH COWAN UNIVERSITY   (Australia)

^g Sir James McCusker Alzheimer's Research Unit   (Australia)

^h AUSTIN HEALTH   (Australia)

ⁱ NATIONAL AGEING RESEARCH INSTITUTE   (Australia)

^j CRC for Mental Health   (Australia)

more..

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DIAGNOSIS; FORECASTING; NEURODEGENERATIVE DISEASES; REGRESSION ANALYSIS; RISK ASSESSMENT;

ALZHEIMER'S DISEASE; AREA UNDER THE ROC CURVE; DISEASE DEVELOPMENT; LOGISTIC REGRESSIONS; NEUROPSYCHOLOGICAL TESTS; REGRESSION MODEL; SIGNIFICANT VARIABLES; STATISTICAL COMPARISONS;

GENETIC ALGORITHMS;

ALGORITHM; ALZHEIMER DISEASE; COMPUTER SIMULATION; DISEASE COURSE; GENETICS; HUMAN; MILD COGNITIVE IMPAIRMENT; RECEIVER OPERATING CHARACTERISTIC; RISK FACTOR; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; ALZHEIMER DISEASE; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; DISEASE PROGRESSION; HUMANS; LOGISTIC MODELS; MILD COGNITIVE IMPAIRMENT; RISK FACTORS; ROC CURVE;

EID: 84938844422     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-15-S16-S11     Document Type: Article

References (36)

1. Barnes D, Yaffe K The projected effect of risk factor reduction on. Alzheimer's disease prevalence. Lancet Neurol 2011, 10(9):819-828.
2. Rowe CC, Bourgeat P, Ellis KA, Brown B, Lim Y, Mulligan R, Maruff P,. Woodward M, Price R, Robins P, Tochon-Danguy H, O'Keefe C, Pike KE,. Yates P, Szoeke C, Salvado O, Macaulay SL, O'Meara T, Head R, Cobiac L,. Savage G, Martins RL, Masters CL, Ames D, Villemagne VL. Predicting. Alzheimer disease with β-amyloid imaging: Results from the Australian. imaging, biomarkers, and lifestyle study of ageing. Ann Neurol 2013,. 74(6):905-913.
3. Ellis KA, Szoeke C, Bush AI, Darby D, Graham PL, Lautenschlager NT,. Macaulay SL, Martins RL, Maruff P, Masters CL, McBride SJ, Pide KE, Rainey-. Smith SR, Rembach A, Robertson J, Rowe CC, Savage G, Villemagne VL,. Woodward M, Wilson W, Zhang P, Ames D: Rates of diagnostic transition. and cognitive change at 18-month follow-up among 1112 participants. in the Australian Imaging, Biomarkers and Lifestyle Flagship Study of. Ageing(AIBL). Int Psychogeriatr 2013, 20:1-12.
4. Weiner N, Veitch D, Aisen P, Beckett L, Cairns N, Green R, Harvey D, Jack C,. Jagust W, Liu E, Morris J, Petersen R, Saykin A, Schmidt M, Shaw L, Shen L,. Siuciak J, Soares H, Toga A, Trojanowski J: The Alzheimer's Disease. Neuroimaging Initiative: a review of papers published since its. inception. Alzheimers Dement 2012, 8(Suppl):S1-S68.
5. Eskildsen S, Coupe P, Garcia-Lorenzo D, Fonov V, Pruessner J, Collins D:. Prediction of Alzheimer's disease in subjects with mild cognitive. impairment from the ADNI cohort using patterns of cortical thinning. Neuroimage 2013, 65:511-521.
6. Petersen R, Aisen P, Boeve B, Geda Y, Ivnik R, Knopman D, Mielke M,. Pankratz V, Roberts R, Rocca W, Weigand S, Weiner M, Wiste H, Jack C:. Criteria for mild cognitive impairment due to Alzheimer's disease in the. community. Ann Neurol 2013, May 20 doi:10.1002/ana.23931.
7. Roberts R, Geda Y, Knopman D, Cha R, Pankratz V, Boeve B, Tangalos E, Ivnik R,. Rocca W, Petersen R: The incidence of MCI differs by subtype and is hegher. in men: the Mayo Clinic Study of Aging. Neurology 2012, 78(5):342-351.
8. Gomar J, Bobes-Bascaran M, Conejero-Goldberg C, Davies P, Goldberg T:. Utility of combinations of biomarkers, cognitive markers, and risk factors. to predict conversion from mild cognitive impairment to Alzheimer. disease in patients in the Alzheimer's Disease Neuroimaging Initiative. Arch Gen Psychiatry 2011, 68:961-969.
9. Hendrix S, Welsh-Bohmer K: "Separation of cognitive domains to improve. prediction of progression from mild cognitive impairment to Alzheimer's. disease,". Alzheimer's Res Ther 2013, 5(3):22.
10. Trzepacz P, Yu P, Sun J, Schuh K, Case K, Witte M, Hochstetler H, Kake A:. Comparison of neuroimaging modalities for the prediction of. conversion from mild cognitive impairment to Alzheimer's dementia. Neurobiol Aging 2014, 35(1):143-151.
11. Young J, Modat M, Cardoso M, Mendelson A, Cash D, Ourselin S: Accurate. multimodal probabilistic prediction of conversion to Alzheimer's disease in. patients with mild cognitive impairment. Neuroimage Clin 2013, 2:735-745.
12. Harrington K, Lim Y, Ellis KA, Copolov C, Darby D, Weinborn M, Ames D,. Martins RL, Savage D, Szoeke C, Rowe CC, Villemagne VL, Masters CL,. Maruff P: The association of Aβ amyloid and composite cognitive. measures in healthy older adults and MCI. Int Psychogeriatr 2013,. 25(10):1667-1677.
13. Lim Y, Ellis KA, Harrington K, Kamer A, Pietrzak R, Bush AI, Darby D,. Martins RL, Masters CL, Rowe CC, Savage G, Szoeke C, Villemagne VL,. Ames D, Maruff P: Cognitive consequences of high Aβ amyloid in mild. cognitive impairment and healthy older adults: implications for early. detection of Alzheimer's. Neuropsychology 2013, 27(3):322-332.
14. Ellis KA, Zhang P, Fahey M, Martins R, Maruff P, Masters C, Macauley L,. Rowe C, Svage G, Szoeke C, Taddei K, Villemagne V and Ames D: Predictors. of Cognitive Decline at 18 Month Follow Up among 1112 Participants in. the Australian Imaging, Biomarkers and Lifestyle Flagship Study of. Ageing (AIBL). Alzheimer's & Dementia 2011, 7(4):S158.
15. Sivanandam S, Deepa S: Introduction to Genetic Algorithms. Springer; 2008. .
16. Gayou O, Das SK, Zhou SM, Marks LB, Parda DS, Miften M: A genetic. algorithm for variable selection in logistic regression analysis of. radiotherapy treatment outcomes. American Association of Physicists in. Medicine 2008, 35(12):5426-5433,.
17. Vinterbo S, Ohno-Machado L: A genetic algorithm to select variables in. logistic regression: example in the domain of myocardial infarction. In. Proceedings of the AMIA Annual Symposium: November 6-10, 1999; Washington. DC. American Medical Informatics Association;Nancy M. Lorenzi 1999:984-988.
18. Folstein MF, Folstein SE, McHugh PR. "Mini-mental state": a practical. method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research 1975, 12:189-198.
19. Wechsler D: A standardised memory scale for clinical use. Journal of. Psychology 1945, 19:87-95.
20. Delis DC, Kaplan EA, Kramer JH. The Delis-Kaplan Executive Function System. (D-KEFS) San Antonio TX: Psychological Corporation; 2001.
21. Meyers JE, Meyers KR. Rey Complex Figure Test and Recognition Trial. Professional Manual Psychological Assessment Resource, Inc; 1995.
22. Saxton J, Ratcliff G, Munro CA, Coffery EC, Becker JT, Fried L, Kuller L:. Normative data on the Boston Naming Test and two equivalent 30-item. short forms. Clinical Neuropsychology 2000, 14:526-534.
23. Wechsler D: Wechsler Adult Intelligence Scale. 3 edition. (WAIS-III) San. Antonio, TX: Psychological Corporation; 1997.
24. Strauss E, Sherman E, Spreen O: A Compendium of Neuropsychological Tests:. Administration, Norms, and Commentary. 3 edition. New York: Oxford. University Press; 2006.
25. Freedman M, Leach L, Kaplan E, Winocur G, Shulman K, Delis DC. Clock. Drawing: A Neuropsychological Analysis New York: Oxford University; 1994.
26. Wechsler D: Wechsler Test of Adult Reading: Examiner's Manual. San Antonio,. TX: The Psychological Corporation; 2001.
27. Burke W, Miller J, Rubin E, Morris J, Coben J, Duchek J, Wittels I, Berg L:. Reliability of the Washington University Clinical Dementia Rating. Archives of Neurology 1988, 45(1):31-32.
28. Zigmond A, Snaith R: The hospital anxiety and depression scale. Acta. Psychiatrica Scandinavica 1983, 67(6):361-370.
29. Ellis KA, Bush AI, Darby D, De Fazio D, Foster J, Hudson P, Lautenschlager N,. Lenzo N, Martins RL, Maruff P, Masters CL, Milner A, Pike KE, Rower CC,. Savage G, Szoeke C, Taddei K, Villemagne VL, M. Woodward M, Ames D. The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging:. methodology and baseline characteristics of 1112 individuals recruited. for a longitudinal study of Alzheimer's disease. Int Psychogeriatr 2009,. 21(4):672-687.
30. Fvan Rooij AJF, Jain LC. Neural Network Training Using Genetic Algorithms. World Scientific publishing Co. Pre. Ltd; 1996.
31. Gandhi S, Khan D, Solanki VS A Comparative Analysis of Selection. Scheme. International Journal of Soft Computing and Engineering 2012,. 2(4):131-134.
32. Parker B, Günter S, Bedo J: Stratification bias in low signal microarray. studies. BMC Bioinformatics 2007, 8:326.
33. Metz CE: ROC methodology in radiologic imaging. Science 1988,. 21:720-733.
34. Swets JA: Measuring the accuracy of diagnostic systems. Science 1988,. 240(4857):1285.
35. Development Core Team, R: A language and environment for statistical. computing R Foundation for Statistical Computing, Vienna, Austria; 2009.
36. Akaike H: A new look at the statistical method identification. IEEE. Transactions on Automatic Control 1974, 19:716-723.