The interaction of multidimensional person, environment, task, and performance factors requires advanced analytic approaches to understand livability among older adults. This study examined the predictive value of the Livability Scale (LS) for residential satisfaction using multiple machine learning algorithms in adults aged 65 and above. Six models—logistic regression, decision tree, random forest, gradient boosting, support vector machine, and an ensemble model—were compared to determine which best captured the LS complex structure. The random forest demonstrated the strongest performance (F1 = 0.74, AUC = 0.78) with consistent generalization in k-fold cross-validation (M = 0.68), providing balanced sensitivity (0.60) and specificity (0.90). Model comparison also revealed meaningful differences: SVM showed heightened sensitivity to satisfaction, correctly detecting a larger number of satisfied cases but with increased false positives, whereas GBM minimized false positives, indicating higher specificity but lower sensitivity. Feature-importance analysis identified cultural facilities, residential affordability, activities and events, parks, structural safety, and cleanliness as key contributors to predictions. Overall, the findings emphasize the value of machine learning in uncovering non-linear patterns in livability data and demonstrate the potential of algorithmic diversity to guide evidence-based residential interventions for older adults.
AllenM. S.IliescuD.GreiffS. (2022). Single item measures in psychological science. European Journal of Psychological Assessment, 38(1), 1–5. https://doi.org/10.1027/1015-5759/a000699
2.
BradleyA. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145–1159. https://doi.org/10.1016/S0031-3203(96)00142-2
BreimanL.FriedmanJ. H.OlshenR. A.StoneC. J. (1984). Classification and regression trees. Wadsworth.
5.
BrivioP.PaladiniM. S.RacagniG.RivaM. A.CalabreseF.MolteniR. (2019). From healthy aging to frailty: In search of the underlying mechanisms. Current Medicinal Chemistry, 26(20), 3685–3701. https://doi.org/10.2174/0929867326666190717152739
Costa-FontJ. (2013). Housing-related well-being in older people: The impact of environmental and financial influences. Urban Studies, 50(4), 657–673. https://doi.org/10.1177/0042098012456247
8.
CrammJ. M.van DijkH. M.NieboerA. P. (2018). The creation of age-friendly environments is especially important to frail older people. Ageing and Society, 38(4), 700–720. https://doi.org/10.1017/S0144686X16001240
9.
DietterichT. G. (2000). Multiple classifier systems: International workshop. Springer.
10.
DikkenJ.Van den HovenR. F. M.Van StaalduinenW. H.Hulsebosch-JanssenL. M. T.Van HoofJ. (2020). How older people experience the age-friendliness of their city: Development of the age-friendly cities and communities questionnaire. International Journal of Environmental Research and Public Health, 17(18), 6867. https://doi.org/10.3390/ijerph17186867
11.
DsouzaN.Carroll-ScottA.BilalU.HeadenI. E.ReisR.Martinez-DonateA. P. (2023). Investigating the measurement properties of livability: A scoping review. Cities & Health, 7(5), 839–853. https://doi.org/10.1080/23748834.2023.2202894
12.
FerrucciL.Gonzalez-FreireM.FabbriE.SimonsickE.TanakaT.MooreZ.SalimiS.SierraF.de CaboR. (2020). Measuring biological aging in humans: A quest. Aging Cell, 19(2), Article e13080. https://doi.org/10.1111/acel.13080
13.
FornaraF.MancaS. (2016). Healthy residential environments for the elderly. In Handbook of environmental psychology and quality of life research (pp. 441–465). Springer International Publishing.
14.
FryD. E.PineM.NedzaS. M.LockeD. G.RebandA. M.PineG. (2017). Risk-adjusted hospital outcomes in medicare total joint replacement surgical procedures. Journal of Bone and Joint Surgery, 99(1), 10–18. American Volume. https://doi.org/10.2106/JBJS.15.01455
15.
GoutteC.GaussierE. (2005). A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. In European conference on information retrieval (pp. 345–359). Springer.
16.
HastieT.TibshiraniR.FriedmanJ. H. (2009). The elements of statistical learning: Data mining, inference, and prediction. Springer.
HuynhX. P.ParkS. M.KimY. G. (2016, November). Detection of driver drowsiness using 3D deep neural network and semi-supervised gradient boosting machine. In Asian conference on computer vision (pp. 134–145). Springer.
19.
IwarssonS. (2013). Implementation of research-based strategies to foster person-environment fit in housing environments: Challenges and experiences during 20 years. In Environmental gerontology (pp. 69–78). Routledge.
20.
KahanaE.LovegreenL.KahanaB.KahanaM. (2003). Person, environment, and person-environment fit as influences on residential satisfaction of elders. Environment and Behavior, 35(3), 434–453. https://doi.org/10.1177/0013916503035003007
21.
KhorramiZ.YeT.SadatmoosaviA.MirzaeeM.Fadakar DavaraniM. M.KhanjaniN. (2021). The indicators and methods used for measuring urban liveability: A scoping review. Reviews on Environmental Health, 36(3), 397–441. https://doi.org/10.1515/reveh-2020-0097
22.
KimS. Y.KwonS. M. (2021). The determinants and changes of residential satisfaction with apartments: Analyzing housing and residential environment satisfaction using the Korea housing survey in the Ministry of Land, Infrastructure and Transport. Seoul Housing Urban Research & Insight, 11(1), 61–67. https://doi.org/10.26700/shuri.2021.4.11.1.61
23.
KouR.HunterR. F.ClelandC.EllisG. (2021). Physical environmental factors influencing older adults’ park use: A qualitative study. Urban Forestry and Urban Greening, 65, Article 127353. https://doi.org/10.1016/j.ufug.2021.127353
KunchevaL. I. (2014). Combining pattern classifiers: Methods and algorithms. John Wiley & Sons.
26.
LarracyR.PhinyomarkA.SchemeE. (2021, November). Machine learning model validation for early stage studies with small sample sizes. In 2021 43rd annual international conference of the IEEE engineering in medicine & biology society (EMBC) (pp. 2314–2319). IEEE.
27.
LawlessM. T.TieuM.FeoR.KitsonA. L. (2021). Theories of self-care and self-management of long-term conditions by community-dwelling older adults: A systematic review and meta-ethnography. Social Science & Medicine, 287, Article 114393. https://doi.org/10.1016/j.socscimed.2021.114393
28.
LawtonM. P.NahemowL. (1973). Ecology and the aging process. In EisdorferC.LawtonM. P. (Eds.), The psychology of adult development and aging (pp. 619–674). American Psychological Association. https://doi.org/10.1037/10044-020
29.
LeiM.LomaxR. G. (2005). The effect of varying degrees of nonnormality in structural equation modeling. Structural Equation Modeling, 12(1), 1–27. https://doi.org/10.1207/s15328007sem1201_1
30.
LimY. M. (2024a). Systematic review of assessment tools for the housing environment of the old adults population. Therapeutic Science for Rehabilitation, 13(2), 27–40. https://doi.org/10.22683/tsnr.2024.13.2.027
31.
LimY. M. (2024b). Validity and reliability analysis of the livability scale using Rasch analysis. In Asian Conference on Therapeutic Science, Seoul, South Korea.
32.
LimY. M. (2025). Content validity study for the development of an assessment tool measuring livability in older adults. British Journal of Occupational Therapy, Article 03080226251400617. https://doi.org/10.1177/03080226251400617
PowersD. M. (2020). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv preprint arXiv:2010.16061. https://doi.org/10.48550/arXiv.2010.16061
RokachL.MaimonO. (2005). Top-down induction of decision trees classifiers-a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 35(4), 476–487. https://doi.org/10.1109/TSMCC.2004.843247
39.
SeoB. K.KimG.KimD. (2023). The incongruence between objective and subjective rental affordability: Does residential satisfaction matter?Cities, 141, Article 104471. https://doi.org/10.1016/j.cities.2023.104471
40.
StephensC.SzabóÁ.AllenJ.AlpassF. (2019). Livable environments and the quality of life of older people: An ecological perspective. The Gerontologist, 59(4), 675–685. https://doi.org/10.1093/geront/gny043
41.
TabachnickB. G.FidellL. S.UllmanJ. B. (2007). Using multivariate statistics (Vol. 5, No. 7). Pearson.
42.
Van RijsbergenC. (1979, September). Information retrieval: Theory and practice. In Proceedings of the joint IBM/University of Newcastle upon tyne seminar on data base systems(Vol. 79, pp. 1–14). IBM United Kingdom ScientificCentre.
43.
Van StraatenB.Van der LaanJ.RodenburgG.BoersmaS. N.WolfJ. R.Van de MheenD. (2017). Dutch homeless people 2.5 years after shelter admission: What are predictors of housing stability and housing satisfaction?Health and Social Care in the Community, 25(2), 710–722. https://doi.org/10.1111/hsc.12361
44.
VerkoeijenP. P.PolakM. G.BouwmeesterS. (2018). A practical illustration of methods to deal with potential outliers: a multiverse outlier analysis of study 3 from Brummelman, Thomaes, Orobio de Castro, Overbeek, and Bushman. Collabra: Psychology, 4(1), 30. https://doi.org/10.1525/collabra.118
45.
WangQ.MaY.ZhaoK.TianY. (2022). A comprehensive survey of loss functions in machine learning. Annals of Data Science, 9(2), 187–212. https://doi.org/10.1007/s40745-020-00253-5
46.
World Health Organization. (2007). Global age-friendly cities: A guide.
47.
World Health Organization. (2018). The global network for age-friendly cities and communities: Looking back over the last decade, looking forward to the next.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
