Abstract
Alzheimer’s disease is becoming a global epidemic. Its impact is devastating for patients’, their families and the economy. As such, it is important to build good prognostic models that can predict conversion to dementia so that treatment measures could be taken. In this work, we applied a genetic algorithm to choose the most relevant neuropsychological and demographic features for prognostic prediction. The results show improvements over other feature selection methods, with our model being able to predict conversion to dementia with AUC and sensitivity of 88% . Moreover, we found that with only 7 features it is possible to achieve good classification results. These results could help physicians to adjust treatment and select which exams should be performed regularly to increase efficiency in clinical practice.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Barker, W.W., et al.: Relative frequencies of Alzheimer disease, Lewy body, vascular and Frontotemporal dementia, and Hippocampal sclerosis in the State of Florida Brain Bank. Alzheimer Dis. Assoc. Disord. 16, 203–212 (2002)
Hebert, L.E., Scherr, P.A., Bienias, J.L., Bennett, D.A., Evans, D.A.: Alzheimer disease in the US population: prevalence estimates using the 2000 census. Arch. Neurol. 60, 1119 (2003)
Prince, M., Comas-Herrera, A., Knapp, M., Guerchet, M., Karagiannidou, M.: World Alzheimer Report 2016 Improving healthcare for people living with dementia (2016)
Roberts, R., Knopman, D.S.: Classification and epidemiology of MCI. Clin. Geriatr. Med. 29, 753–772 (2013)
Silva, D., Guerreiro, M., Santana, I., Rodrigues, A., Cardoso, S., Maroco, J., De Mendonça, A.: Prediction of long-term (5 years) conversion to dementia using neuropsychological tests in a memory clinic setting. J. Alzheimer’s Dis. 34, 681–689 (2013)
Silva, D., Guerreiro, M., Maroco, J., Santana, I., Rodrigues, A., Bravo Marques, J., de Mendonça, A.: Comparison of four verbal memory tests for the diagnosis and predictive value of mild cognitive impairment. Dement. Geriatr. Cogn. Dis. Extra 2, 120–131 (2012)
Kolibas, E., Korinkova, V., Novotny, V., Vajdickova, K., Hunakova, D.: ADAS-cog (Alzheimer’s Disease Assessment Scale-cognitive subscale)–validation of the Slovak version. Bratisl. Lek. Listy 101, 598–602 (2000)
Folstein, M.F., Folstein, S.E., McHugh, P.R.: Mini-mental state. J. Psychiatr. Res. 12, 189–198 (1975)
Chapman, R.M., Mapstone, M., Mccrary, J.W., Gardner, M.N., Porsteinsson, A., Sandoval, T.C., Reilly, L.A.: Predicting conversion from mild cognitive impairment to Alzheimer´s disease using neuropsychological test and multivariate methods. J. Clin. Exp. Neuropsychol. 33, 187–199 (2012)
Lee, S.J., Ritchie, C.S., Yaffe, K., Cenzer, I.S., Barnes, D.E.: A clinical index to predict progression from mild cognitive impairment to dementia due to Alzheimer’s disease. PLoS ONE 9, 1–15 (2014)
Yang, J., Honavar, V.: Feature subset selection using a genetic algorithm. In: Feature Extraction, Construction and Selection, pp. 117–136. Springer, Boston (1998)
Vandewater, L., Brusic, V., Wilson, W., Macaulay, L., Zhang, P.: An adaptive genetic algorithm for selection of blood-based biomarkers for prediction of Alzheimer’s disease progression. BMC Bioinf. 16, S1 (2015)
Spedding, A.L., Di Fatta, G., Cannataro, M.: A genetic algorithm for the selection of structural MRI features for classification of mild cognitive impairment and Alzheimer’s disease. In: Proceedings of the 2015 IEEE International Conference on Bioinformatics and Biomedicine, pp. 1566–1571 (2015)
Johnson, P., et al.: Genetic algorithm with logistic regression for prediction of progression to Alzheimer’s disease. BMC Bioinf. 15(Suppl. 1), S11 (2014)
Guerreiro, M.: Contributo da Neuropsicologia para o Estudo das Demências, Ph.D. thesis, University of Lisbon (1998)
Grande, G., et al.: Reversible mild cognitive impairment: the role of comorbidities at baseline evaluation. J. Alzheimer’s Dis. 51, 57–67 (2016)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software. ACM SIGKDD Explor. Newsl. 11, 10 (2009)
Hall, M.: Correlation-based feature selection for machine learning. Methodology, pp. 1–5 (1999)
Eskildsen, S.F., Coupé, P., Fonov, V.S., Pruessner, J.C., Collins, D.L.: Structural imaging biomarkers of Alzheimer’s disease: predicting disease progression. Neurobiol. Aging 36, S23–S31 (2015)
Mueller, S.G., Weiner, M.W., Thal, L.J., Petersen, R.C., Jack, C.R., Jagust, W., Trojanowski, J.Q., Toga, A.W., Beckett, L.: Ways toward an early diagnosis in Alzheimer’s disease: the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Alzheimers. Dement. 1, 55–66 (2005)
Acknowledgments
This work was partially supported by FCT under the projects NEUROCLINOMICS2 (PTDC/EEI-SII/1937/2014) and UID/CEC/50021/2013, and an individual doctoral grant to FF (SFRH/BD/118872/2016).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Ferreira, F.L., Cardoso, S., Silva, D., Guerreiro, M., de Mendonça, A., Madeira, S.C. (2017). Improving Prognostic Prediction from Mild Cognitive Impairment to Alzheimer’s Disease Using Genetic Algorithms. In: Fdez-Riverola, F., Mohamad, M., Rocha, M., De Paz, J., Pinto, T. (eds) 11th International Conference on Practical Applications of Computational Biology & Bioinformatics. PACBB 2017. Advances in Intelligent Systems and Computing, vol 616. Springer, Cham. https://doi.org/10.1007/978-3-319-60816-7_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-60816-7_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60815-0
Online ISBN: 978-3-319-60816-7
eBook Packages: EngineeringEngineering (R0)