Abstract
Frailty appears to be the most problematic expression of elderly people. Frail older adults have a high risk of mortality, hospitalization, disability and other adverse outcomes, resulting in burden to individuals, their families, health care services and society. Early detection and screening would help to deliver preventive interventions and reduce the burden of frailty. For this purpose, several studies have been conducted to detect frailty that demonstrates its association with mortality and other health outcomes. Most of these studies have concentrated on the possible risk factors associated with frailty in the elderly population; however, efforts to identify and predict groups of elderly people who are at increased risk of frailty is still challenging in clinical settings. In this paper, Genetic Programming (GP) is exploited to detect and define frailty based on the whole elderly population of the Piedmont, Italy, using administrative databases of clinical characteristics and socio-economic factors. Specifically, GP is designed to predict frailty according to the expected risk of mortality, urgent hospitalization, disability, fracture, and access to the emergency department. The performance of GP model is evaluated using sensitivity, specificity, and accuracy metrics by dividing each dataset into a training set and test set. We find that GP shows competitive performance in predicting frailty compared to the traditional machine learning models. The study demonstrates that the proposed model might be used to screen future frail older adults using clinical, psychological and socio-economic variables, which are commonly collected in community healthcare institutions.
Keywords
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
Kojima, G., Liljas, A., Iliffe, S.: Frailty syndrome: implications and challenges for health care policy. Risk Manag. Healthc. Policy 12, 23–30 (2019). https://doi.org/10.2147/RMHP.S168750
Comans, T.A., Peel, N.M., Hubbard, R.E., Mulligan, A.D., Gray, L.C., Scuffham, P.A.: The increase in healthcare costs associated with frailty in older people discharged to a post-acute transition care program. Age Ageing 45, 317–320 (2016). https://doi.org/10.1093/ageing/afv196
Clegg, A., Young, J., Iliffe, S., Rikkert, M.O., Rockwood, K.: Frailty in elderly people. Lancet 381, 752–762 (2013). https://doi.org/10.1016/S0140-6736(12)62167-9
Wennberg, D., Siegel, M., Darin, B., Filipova, N.: Combined predictive model: final report and technical documentation (2006)
Lally, F., Crome, P.: Understanding frailty (2007). https://doi.org/10.1136/pgmj.2006.048587
Fried, L.P., et al.: Frailty in older adults: evidence for a phenotype. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 56, M146–M157 (2001). https://doi.org/10.1093/gerona/56.3.M146
Rockwood, K., et al.: A global clinical measure of fitness and frailty in elderly people. CMAJ 173, 489–495 (2005). https://doi.org/10.1503/cmaj.050051
Kotsiantis, S.B., et al.: Machine learning: a review of classification and combining techniques. Artif. Intell. Rev. 26, 159–190 (2006). https://doi.org/10.1007/s10462-007-9052-3
Rockwood, K., Andrew, M., Mitnitski, A.: A comparison of two approaches to measuring frailty in elderly people. J. Gerontol. Ser. A Biol. Sci. Med. Sci. 62, 738–743 (2007). https://doi.org/10.1093/gerona/62.7.738
Blodgett, J., Theou, O., Kirkland, S., Andreou, P., Rockwood, K.: Frailty in NHANES: comparing the frailty index and phenotype. Arch. Gerontol. Geriatr. 60, 464–470 (2015). https://doi.org/10.1016/j.archger.2015.01.016
Theou, O., Brothers, T.D., Mitnitski, A., Rockwood, K.: Operationalization of frailty using eight commonly used scales and comparison of their ability to predict all-cause mortality. J. Am. Geriatr. Soc. 61, 1537–1551 (2013). https://doi.org/10.1111/jgs.12420
Katz, A., Wong, S., Williamson, T., Taylor, C., Peterson, S.: Identification of frailty using EMR and admin data: a complex issue. Int. J. Popul. Data Sci. 3 (2018). https://doi.org/10.23889/ijpds.v3i4.832
Chen, C.-Y., Wu, S.-C., Chen, L.-J., Lue, B.-H.: The prevalence of subjective frailty and factors associated with frailty in Taiwan. Arch. Gerontol. Geriatr. 50, S43–S47 (2010). https://doi.org/10.1016/s0167-4943(10)70012-1
Lee, D.H., Buth, K.J., Martin, B.J., Yip, A.M., Hirsch, G.M.: Frail patients are at increased risk for mortality and prolonged institutional care after cardiac surgery. Circulation 121, 973 (2010). https://doi.org/10.1161/CIRCULATIONAHA.108.841437
Homer, M.L., Palmer, N.P., Fox, K.P., Armstrong, J., Mandl, K.D.: Predicting falls in people aged 65 years and older from insurance claims. Am. J. Med. 130, 744.e17–744.e23 (2017). https://doi.org/10.1016/j.amjmed.2017.01.003
Bertini, F., Bergami, G., Montesi, D., Veronese, G., Marchesini, G., Pandolfi, P.: Predicting frailty condition in elderly using multidimensional socioclinical databases. Proc. IEEE 106, 723–737 (2018). https://doi.org/10.1109/JPROC.2018.2791463
Amari, S.: Machine learning. In: Amari, S. (ed.) Information Geometry and Its Applications. AMS, vol. 194, pp. 231–278. Springer, Tokyo (2016). https://doi.org/10.1007/978-4-431-55978-8_11
Japkowicz, N., Stephen, S.: The class imbalance problem: a systematic study. Intell. Data Anal. 6, 429–449 (2018). https://doi.org/10.3233/ida-2002-6504
Barandela, R., Sánchez, J.S., García, V., Rangel, E.: Strategies for learning in class imbalance problems. Pattern Recogn. 36, 849–851 (2003). https://doi.org/10.1016/S0031-3203(02)00257-1
McCarthy, K., Zabar, B., Weiss, G.: Does cost-sensitive learning beat sampling for classifying rare classes? In: Proceedings of the 1st International Workshop on Utility-based Data Mining - UBDM 2005, pp. 69–77. ACM Press, New York (2005). https://doi.org/10.1145/1089827.1089836
Chen, J.X., Cheng, T.H., Chan, A.L.F., Wang, H.Y.: An application of classification analysis for skewed class distribution in therapeutic drug monitoring - the case of vancomycin. In: Proceedings - IDEAS Workshop on Medical Information Systems: The Digital Hospital, IDEAS 2004-DH (2005)
Orriols, A., Bernadí-Mansilla, E.: Class imbalance problem in UCS classifier system: fitness adaptation. In: 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005, Proceedings (2005)
Azimlu, F., Rahnamayan, S., Makrehchi, M., Kalra, N.: Comparing genetic programming with other data mining techniques on prediction models. In: 2019 14th International Conference on Computer Science & Education (ICCSE), pp. 785–791. IEEE (2019). https://doi.org/10.1109/ICCSE.2019.8845381
Amal, S., Periwal, V., Scaria, V.: Predictive modeling of anti-malarial molecules inhibiting Apicoplast formation. BMC Bioinf. 14, 55 (2013). https://doi.org/10.1186/1471-2105-14-55
Haixiang, G., Yijing, L., Shang, J., Mingyun, G., Yuanyue, H., Bing, G.: Learning from class-imbalanced data: review of methods and applications. Expert Syst. Appl. 73, 220–239 (2017). https://doi.org/10.1016/j.eswa.2016.12.035
Kang, Q., Chen, X.S., Li, S.S., Zhou, M.C.: A noise-filtered under-sampling scheme for imbalanced classification. IEEE Trans. Cybern. 47, 4263–4274 (2017). https://doi.org/10.1109/TCYB.2016.2606104
Leevy, J.L., Khoshgoftaar, T.M., Bauder, R.A., Seliya, N.: A survey on addressing high-class imbalance in big data. J. Big Data 5(1), 1–30 (2018). https://doi.org/10.1186/s40537-018-0151-6
Han, J., Kamber, M., Pei, J.: Data Mining. Elsevier, Amsterdam (2012). https://doi.org/10.1016/C2009-0-61819-5
Volrathongchai, K., Brennan, P.F., Ferris, M.C.: Predicting the likelihood of falls among the elderly using likelihood basis pursuit technique. In: AMIA Annual Symposium, Proceedings (2005)
Bannister, C.A., Halcox, J.P., Currie, C.J., Preece, A., Spasić, I.: A genetic programming approach to development of clinical prediction models: a case study in symptomatic cardiovascular disease. PLoS One (2018). https://doi.org/10.1371/journal.pone.0202685
Bannister, C.A., Currie, C.J., Preece, A., Spasic, I.: Automatic development of clinical prediction models with genetic programming: a case study in cardiovascular disease. Value Health 17, A200–A201 (2014). https://doi.org/10.1016/j.jval.2014.03.1171
Poli, R., Koza, J.: Genetic programming. In: Burke, E., Kendall, G. (eds.) Search Methodologies, pp. 143–185. Springer, Boston (2014). https://doi.org/10.1007/978-1-4614-6940-7_6
HeuristicLab homepage. https://dev.heuristiclab.com/trac.fcgi/wiki
Vluymans, S.: Learning from imbalanced data. In: Studies in Computational Intelligence, pp. 81–110 (2019). https://doi.org/10.1007/978-3-030-04663-7_4
Ulloa-Cazarez, R.L., López-Martín, C., Abran, A., Yáñez-Márquez, C.: Prediction of online students performance by means of genetic programming. Appl. Artif. Intell. 32, 858–881 (2018). https://doi.org/10.1080/08839514.2018.1508839
Can, B., Heavey, C.: A comparison of genetic programming and artificial neural networks in metamodeling of discrete-event simulation models. Comput. Oper. Res. 39, 424–436 (2012). https://doi.org/10.1016/j.cor.2011.05.004
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Tarekegn, A., Ricceri, F., Costa, G., Ferracin, E., Giacobini, M. (2020). Detection of Frailty Using Genetic Programming. In: Hu, T., Lourenço, N., Medvet, E., Divina, F. (eds) Genetic Programming. EuroGP 2020. Lecture Notes in Computer Science(), vol 12101. Springer, Cham. https://doi.org/10.1007/978-3-030-44094-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-44094-7_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44093-0
Online ISBN: 978-3-030-44094-7
eBook Packages: Computer ScienceComputer Science (R0)