Utility of Environmental Complexity as a Predictor of Alzheimer’s Disease Diagnosis: A Big-Data Machine Learning Approach - 21/11/24

Doi : 10.14283/jpad.2023.18 
M. Yuan 1, Kristen M. Kennedy 2,
1 Department of Geospatial Information Sciences, School of Economic, Political and Policy Sciences, The University of Texas at Dallas, Richardson, TX, USA 
2 Department of Psychology, School of Behavioral and Brain Sciences, Center for Vital Longevity, The University of Texas at Dallas, 75235, Dallas, TX, USA 

b Kristen.kennedy1@utdallas.edu Kristen.kennedy1@utdallas.edu

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Abstract

Background

Rural-urban differences and spatial navigation deficits have received much attention in Alzheimer’s Disease research. While individual environmental and neighborhood factors have been independently investigated, their integrative, multifactorial effects on Alzheimer’s diagnosis have not. Here we explore this “environmental complexity” for predictive power in classifying Alzheimer’s from cognitively-normal status.

Methods

We utilized data from the National Alzheimer’s Coordinating Center (NACC) uniform data set containing annual visits since 2005 and selected individuals with multiple visits and who remained in their zipcode (N = 22,553). We georeferenced each subject with 3-digit zipcodes of their residences since entering the program. We calculated environmental complexity measures using geospatial tools from street networks and landmarks for spatial navigation in subjects’ zipcode zones. Zipcode zones were grouped into two cognitive classes (Cognitively-Normal and Alzheimer’s-inclined) based on the ratios of AD and dementia subjects to all subjects in an individual zipcode zone. We randomly selected 80% of the data to train a neural network classifier model on environmental complexity measures to predict the cognitive class for each zone, controlling for salient demographic variables. The remaining 20% served as the test set for performance evaluation.

Results

Our proposed model reached excellent classification ability on the testing data: 83.87% accuracy, 95.23% precision, 83.33% recall, and 0.8889 F1-score (F1-score=1 for perfect prediction). The most salient features of “Alzheimer’s-inclined” zipcode zones included longer street-length average, higher circuity, and slightly fewer points of interest. Most “cognitively-normal” zipcode zones appeared in or near urban areas with high environmental complexity measures.

Conclusion

Environmental complexity, reflected in frequency and density of street networks and landmarks features, predicted with high precision the cognitive status of 3-digit zipcode zones based on the etiologic diagnoses and observed cognitive impairment of NACC subjects residing in these zones. The zipcode zones vary widely in size (1.6 km2 to 35,241 km2), and large zipcode zones suffer high spatial heterogeneity. Other proven AD risk factors, such as PM2.5, disperse across zones, and so do individual’s activities, leading to spatial uncertainty. Nevertheless, the model classifies diagnosis well, establishing the need for prospective experiments to quantify effects of environmental complexity on Alzheimer’s development.

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Key words : Alzheimer’s disease, neural network modelling, environmental complexity, cognitive map, geospatial mapping


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© 2023  THE AUTHORS. Published by Elsevier Masson SAS on behalf of SERDI Publisher.. Publié par Elsevier Masson SAS. Tous droits réservés.
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Vol 10 - N° 2

P. 223-235 - avril 2023 Retour au numéro
Article précédent Article précédent
  • Longitudinal Exposure—Response Modeling of Multiple Indicators of Alzheimer’s Disease Progression
  • D.G. Polhamus, Michael J. Dolton, J.A. Rogers, L. Honigberg, J.Y. Jin, A. Quartino, Alzheimer’s Disease Neuroimaging Initiative (ADNI)
| Article suivant Article suivant
  • Validity of Normative Volumetric Estimates from Open Access Software in Amnestic Mild Cognitive Impairment
  • S. Fountain-Zaragoza, O. Horn, K.E. Thorn, A.Z. Kraal, Andreana Benitez

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