doi: 10.3934/bdia.2017014

Prediction models for burden of caregivers applying data mining techniques

1. 

School of Nursing, Columbia University Medical Center, New York, NY, 10032, USA

2. 

Department of Nursing, Columbia University Medical Center, New York, NY, 10032, USA

3. 

Department of Neurology, Columbia University Medical Center, New York, NY, 10032, USA

* Corresponding author: Sunmoo Yoon, RN, PhD, Associate Research Scientist, Columbia University, sy2102@columbia.edu

Published  November 2017

Introduction

Caregiver stress negatively influences both patients and caregivers. Predictors of caregiver difficulty may provide crucial insights for providers to prioritize those with the highest risk of stress. The purpose of this study was to develop a prediction model of caregiver difficulty by applying data mining techniques to a national behavioral risk factor data set.

Methods

Behavioral data including 397 variables on 2,264 informal caregivers, who provided any care to a friend or family member during the past month, were extracted from a publicly available national dataset in the U.S (N = 451,075) and analyzed. We applied several classification algorithms (J48, RandomForest, MultilayerPerceptron, AdaboostM1), to iteratively generate prediction models for caregiving difficulty with 10-fold cross validation.

Results

44.7% of informal caregivers answered that they faced the greatest difficulties while they took care of patients. Among those who faced the greatest difficulties, the reasons were creating emotional burden (45%). Patient cognitive alteration (e.g. cognitive changes in thinking or remembering during the past year), care hours, and relationship with a caregiver appeared as the main predictors of caregiver stress (classified correctly 63%, difficulty AUC = 65%, no difficulty AUC = 65%).

Conclusions

Data mining methods were useful to discover new behavioral risk knowledge and to visualize predictors of caregiver stress from a multidimensional behavioral dataset.This study suggests that health professionals target dementia family caregivers who are anticipated to experience patients' neuro-cognitive changes, and inform the caregivers about importance of limiting care hours, burn out and delegation of caregiving tasks.

Citation: Sunmoo Yoon, Maria Patrao, Debbie Schauer, Jose Gutierrez. Prediction models for burden of caregivers applying data mining techniques. Big Data & Information Analytics, doi: 10.3934/bdia.2017014
References:
[1]

R. D. AdelmanL. L. TmanovaD. DelgadoS. Dion and M. S. Lachs, Caregiver burden: A clinical review, Jama, 311 (2014), 1052-1060. doi: 10.1001/jama.2014.304.

[2]

A. Barfar and B. Padmanabhan, Predicting presidential election outcomes from what people watch, Big Data, 5 (2017), 32-41. doi: 10.1089/big.2017.0013.

[3]

C. M. Bishop, Neural Networks for Pattern Recognition, Oxford university press, 1995.

[4]

L. Breiman, Random forests, Machine Learning, 45 (2001), 5-32.

[5]

C.-Y. ChiaoH.-S. Wu and C.-Y. Hsiao, Caregiver burden for informal caregivers of patients with dementia: A systematic review, International Nursing Review, 62 (2015), 340-350. doi: 10.1111/inr.12194.

[6]

G. DePalmaH. XuK. E. CovinskyB. A. CraigE. StallardJ. Thomas Ⅲ and L. P. Sands, Hospital readmission among older adults who return home with unmet need for ADL disability, The Gerontologist, 53 (2013), 454-461. doi: 10.1093/geront/gns103.

[7]

C. for Disease Control and Prevention, Behavioral risk factor surveillance system survey data, atlanta, georgia. u. s.

[8]

J. E. GauglerD. L. RothW. E. Haley and M. S. Mittelman, Can counseling and support reduce burden and depressive symptoms in caregivers of people with Alzheimer's disease during the transition to institutionalization? results from the new york university caregiver intervention study, Journal of the American Geriatrics Society, 56 (2008), 421-428. doi: 10.1111/j.1532-5415.2007.01593.x.

[9]

P. E. Greenberg and H. G. Birnbaum, The economic burden of depression in the us: Societal and patient perspectives, Expert Opinion on Pharmacotherapy, 6 (2005), 369-376. doi: 10.1517/14656566.6.3.369.

[10]

S. GuptaG. HawkerA. LaporteR. Croxford and P. Coyte, The economic burden of disabling hip and knee osteoarthritis (oa) from the perspective of individuals living with this condition, Rheumatology, 44 (2005), 1531-1537. doi: 10.1093/rheumatology/kei049.

[11]

M. HallE. FrankG. HolmesB. PfahringerP. Reutemann and I. H. Witten, The weka data mining software: An update, ACM SIGKDD Explorations Newsletter, 11 (2009), 10-18. doi: 10.1145/1656274.1656278.

[12]

Y. LeCunY. Bengio and G. Hinton, Deep learning, Nature, 521 (2015), 436-444. doi: 10.1038/nature14539.

[13]

S. J. LupienB. S. McEwenM. R. Gunnar and C. Heim, Effects of stress throughout the lifespan on the brain, behaviour and cognition, Nature Reviews Neuroscience, 10 (2009), 434-445. doi: 10.1038/nrn2639.

[14]

P. C. J. Navas, Y. C. G. Parra and J. I. R. Molano, Big data tools: Haddop, mongodb and weka, in International Conference on Data Mining and Big Data, Springer, 2016,449-456.

[15]

U. D. of Health Huma Service., 2011 poverty guideline, Federal Register, 76 (2010), 3637-3638.

[16]

B. D. Ripley, Pattern Recognition and Neural Networks, Cambridge university press, 2007.

[17]

J. W. RoweT. Fulmer and L. Fried, Preparing for better health and health care for an aging population, Jama, 316 (2016), 1643-1644. doi: 10.1001/jama.2016.12335.

[18]

J. Schmidhuber, Deep learning in neural networks: An overview, Neural Networks, 61 (2015), 85-117. doi: 10.1016/j.neunet.2014.09.003.

[19]

B. C. Spillman and S. K. Long, Does high caregiver stress predict nursing home entry?, INQUIRY: The Journal of Health Care Organization, Provision, and Financing, 46 (2009), 140-161. doi: 10.5034/inquiryjrnl_46.02.140.

[20]

C. H. Van HoutvenS. D. RamseyM. C. HornbrookA. A. Atienza and M. van Ryn, Economic burden for informal caregivers of lung and colorectal cancer patients, The Oncologist, 15 (2010), 883-893. doi: 10.1634/theoncologist.2010-0005.

[21]

I. H. Witten, E. Frank, M. A. Hall and C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2016.

[22]

X. WuV. KumarJ. Ross QuinlanJ. GhoshQ. YangH. MotodaG. J. McLachlanA. NgB. Liu and P. S. Yu, Top 10 algorithms in data mining, Knowledge and Information Systems, 14 (2008), 1-37. doi: 10.1007/s10115-007-0114-2.

[23]

E. Yan and T. Kwok, Abuse of older Chinese with dementia by family caregivers: An inquiry into the role of caregiver burden, International Journal of Geriatric Psychiatry, 26 (2011), 527-535. doi: 10.1002/gps.2561.

[24]

Q. Yang and X. Wu, 10 challenging problems in data mining research, International Journal of Information Technology & Decision Making, 5 (2006), 597-604. doi: 10.1142/S0219622006002258.

show all references

References:
[1]

R. D. AdelmanL. L. TmanovaD. DelgadoS. Dion and M. S. Lachs, Caregiver burden: A clinical review, Jama, 311 (2014), 1052-1060. doi: 10.1001/jama.2014.304.

[2]

A. Barfar and B. Padmanabhan, Predicting presidential election outcomes from what people watch, Big Data, 5 (2017), 32-41. doi: 10.1089/big.2017.0013.

[3]

C. M. Bishop, Neural Networks for Pattern Recognition, Oxford university press, 1995.

[4]

L. Breiman, Random forests, Machine Learning, 45 (2001), 5-32.

[5]

C.-Y. ChiaoH.-S. Wu and C.-Y. Hsiao, Caregiver burden for informal caregivers of patients with dementia: A systematic review, International Nursing Review, 62 (2015), 340-350. doi: 10.1111/inr.12194.

[6]

G. DePalmaH. XuK. E. CovinskyB. A. CraigE. StallardJ. Thomas Ⅲ and L. P. Sands, Hospital readmission among older adults who return home with unmet need for ADL disability, The Gerontologist, 53 (2013), 454-461. doi: 10.1093/geront/gns103.

[7]

C. for Disease Control and Prevention, Behavioral risk factor surveillance system survey data, atlanta, georgia. u. s.

[8]

J. E. GauglerD. L. RothW. E. Haley and M. S. Mittelman, Can counseling and support reduce burden and depressive symptoms in caregivers of people with Alzheimer's disease during the transition to institutionalization? results from the new york university caregiver intervention study, Journal of the American Geriatrics Society, 56 (2008), 421-428. doi: 10.1111/j.1532-5415.2007.01593.x.

[9]

P. E. Greenberg and H. G. Birnbaum, The economic burden of depression in the us: Societal and patient perspectives, Expert Opinion on Pharmacotherapy, 6 (2005), 369-376. doi: 10.1517/14656566.6.3.369.

[10]

S. GuptaG. HawkerA. LaporteR. Croxford and P. Coyte, The economic burden of disabling hip and knee osteoarthritis (oa) from the perspective of individuals living with this condition, Rheumatology, 44 (2005), 1531-1537. doi: 10.1093/rheumatology/kei049.

[11]

M. HallE. FrankG. HolmesB. PfahringerP. Reutemann and I. H. Witten, The weka data mining software: An update, ACM SIGKDD Explorations Newsletter, 11 (2009), 10-18. doi: 10.1145/1656274.1656278.

[12]

Y. LeCunY. Bengio and G. Hinton, Deep learning, Nature, 521 (2015), 436-444. doi: 10.1038/nature14539.

[13]

S. J. LupienB. S. McEwenM. R. Gunnar and C. Heim, Effects of stress throughout the lifespan on the brain, behaviour and cognition, Nature Reviews Neuroscience, 10 (2009), 434-445. doi: 10.1038/nrn2639.

[15]

U. D. of Health Huma Service., 2011 poverty guideline, Federal Register, 76 (2010), 3637-3638.

[16]

B. D. Ripley, Pattern Recognition and Neural Networks, Cambridge university press, 2007.

[17]

J. W. RoweT. Fulmer and L. Fried, Preparing for better health and health care for an aging population, Jama, 316 (2016), 1643-1644. doi: 10.1001/jama.2016.12335.

[18]

J. Schmidhuber, Deep learning in neural networks: An overview, Neural Networks, 61 (2015), 85-117. doi: 10.1016/j.neunet.2014.09.003.

[19]

B. C. Spillman and S. K. Long, Does high caregiver stress predict nursing home entry?, INQUIRY: The Journal of Health Care Organization, Provision, and Financing, 46 (2009), 140-161. doi: 10.5034/inquiryjrnl_46.02.140.

[20]

C. H. Van HoutvenS. D. RamseyM. C. HornbrookA. A. Atienza and M. van Ryn, Economic burden for informal caregivers of lung and colorectal cancer patients, The Oncologist, 15 (2010), 883-893. doi: 10.1634/theoncologist.2010-0005.

[21]

I. H. Witten, E. Frank, M. A. Hall and C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, 2016.

[22]

X. WuV. KumarJ. Ross QuinlanJ. GhoshQ. YangH. MotodaG. J. McLachlanA. NgB. Liu and P. S. Yu, Top 10 algorithms in data mining, Knowledge and Information Systems, 14 (2008), 1-37. doi: 10.1007/s10115-007-0114-2.

[23]

E. Yan and T. Kwok, Abuse of older Chinese with dementia by family caregivers: An inquiry into the role of caregiver burden, International Journal of Geriatric Psychiatry, 26 (2011), 527-535. doi: 10.1002/gps.2561.

[24]

Q. Yang and X. Wu, 10 challenging problems in data mining research, International Journal of Information Technology & Decision Making, 5 (2006), 597-604. doi: 10.1142/S0219622006002258.

Figure 1.  Iterativesteps of the data mining process to build a prediction model from a large dataset
Figure 2.  Burden of caregivers
Table 1.  Characteristics of Caregivers (n=2,264)
Patient age (mean, SD) 69.87 20.53
Caregiver age (mean, SD) 56.14 15.46
Race/Ethnicity
  White 2,049 90.50%
  Black 61 2.69%
  Hispanic 69 3.05%
  Others 56 2.47%
Patient Gender
  Male 795 35.11%
  Female 1,455 64.27%
Employment
  Employed for wages 1,035 45.72%
  Self-employed 220 9.72%
  Unemployed 423 18.69%
  Retired 582 25.71%
Income
  < $35,000 577 25.49%
  < $50,000 299 13.21%
  < $75,000 344 15.19%
  ≥$75,000 734 32.42%
Relationship
  (Grand) Parents 915 40.41%
  Spouse 371 16.39%
  Child, sibling, relatives 504 22.26%
  Friends 451 19.92%
Patient status
  Cognitive changes 1,156 51.06%
  No cognitive changes 1,038 45.85%
  Not sure 29 1.28%
Patient age (mean, SD) 69.87 20.53
Caregiver age (mean, SD) 56.14 15.46
Race/Ethnicity
  White 2,049 90.50%
  Black 61 2.69%
  Hispanic 69 3.05%
  Others 56 2.47%
Patient Gender
  Male 795 35.11%
  Female 1,455 64.27%
Employment
  Employed for wages 1,035 45.72%
  Self-employed 220 9.72%
  Unemployed 423 18.69%
  Retired 582 25.71%
Income
  < $35,000 577 25.49%
  < $50,000 299 13.21%
  < $75,000 344 15.19%
  ≥$75,000 734 32.42%
Relationship
  (Grand) Parents 915 40.41%
  Spouse 371 16.39%
  Child, sibling, relatives 504 22.26%
  Friends 451 19.92%
Patient status
  Cognitive changes 1,156 51.06%
  No cognitive changes 1,038 45.85%
  Not sure 29 1.28%
Table 2.  Characteristics of Caregivers -Cont'd (n=2,264)
Caregiving duration
  ≤ 1 year 769 33.97%
  ≤ 5 years 907 40.06%
  > 5 years 497 21.95%
Caregiving frequency
  ≤ 10 hours/week 1,344 59.36%
  ≤ 30 hours/week 380 16.78%
  ≤ 100 hours/week 201 8.88%
  > 100 hours/week 92 4.06%
Most needs
  Cleaning, managing $, prepare meals 614 27.12%
  Transportation outside of the home 503 22.22%
  Something else 317 14.00%
  Self care -eating, dressing, bathing 302 13.34%
  Relieving anxiety or depression 184 8.13%
Caregiving difficulties
  No difficulty 1,013 54.0%
  Difficulty 1,178 44.7%
  Not sure/ Don't know 28 1.25%
  Refused 24 1.07%
Greatest difficulties having difficulties
  Creates emotional burden 528 44.82%
  Not enough time for yourself 165 14.01%
  Other difficulty 113 9.59%
  Creates financial burden 95 8.06%
  Affects family relationships 85 7.22%
  No enough time for your family 84 7.13%
  Interferes with your work 71 6.03%
  Aggravates health problems 37 3.14%
Caregiving duration
  ≤ 1 year 769 33.97%
  ≤ 5 years 907 40.06%
  > 5 years 497 21.95%
Caregiving frequency
  ≤ 10 hours/week 1,344 59.36%
  ≤ 30 hours/week 380 16.78%
  ≤ 100 hours/week 201 8.88%
  > 100 hours/week 92 4.06%
Most needs
  Cleaning, managing $, prepare meals 614 27.12%
  Transportation outside of the home 503 22.22%
  Something else 317 14.00%
  Self care -eating, dressing, bathing 302 13.34%
  Relieving anxiety or depression 184 8.13%
Caregiving difficulties
  No difficulty 1,013 54.0%
  Difficulty 1,178 44.7%
  Not sure/ Don't know 28 1.25%
  Refused 24 1.07%
Greatest difficulties having difficulties
  Creates emotional burden 528 44.82%
  Not enough time for yourself 165 14.01%
  Other difficulty 113 9.59%
  Creates financial burden 95 8.06%
  Affects family relationships 85 7.22%
  No enough time for your family 84 7.13%
  Interferes with your work 71 6.03%
  Aggravates health problems 37 3.14%
[1]

Zhuwei Qin, Fuxun Yu, Chenchen Liu, Xiang Chen. How convolutional neural networks see the world --- A survey of convolutional neural network visualization methods. Mathematical Foundations of Computing, 2018, 1 (2) : 149-180. doi: 10.3934/mfc.2018008

[2]

James H. Elder. A new training program in data analytics & visualization. Big Data & Information Analytics, 2016, 1 (1) : i-iii. doi: 10.3934/bdia.2016.1.1i

[3]

Jingmei Zhou, Xiangmo Zhao, Xin Cheng, Zhigang Xu. Visualization analysis of traffic congestion based on floating car data. Discrete & Continuous Dynamical Systems - S, 2015, 8 (6) : 1423-1433. doi: 10.3934/dcdss.2015.8.1423

[4]

Jianfeng Feng, Mariya Shcherbina, Brunello Tirozzi. Stability of the dynamics of an asymmetric neural network. Communications on Pure & Applied Analysis, 2009, 8 (2) : 655-671. doi: 10.3934/cpaa.2009.8.655

[5]

Anupama N, Sudarson Jena. A novel approach using incremental under sampling for data stream mining. Big Data & Information Analytics, 2017, 2 (5) : 1-13. doi: 10.3934/bdia.2017017

[6]

Zhen Mei. Manifold data mining helps businesses grow more effectively. Big Data & Information Analytics, 2016, 1 (2&3) : 275-276. doi: 10.3934/bdia.2016009

[7]

Qinglei Zhang, Wenying Feng. Detecting coalition attacks in online advertising: A hybrid data mining approach. Big Data & Information Analytics, 2016, 1 (2&3) : 227-245. doi: 10.3934/bdia.2016006

[8]

Ying Sue Huang, Chai Wah Wu. Stability of cellular neural network with small delays. Conference Publications, 2005, 2005 (Special) : 420-426. doi: 10.3934/proc.2005.2005.420

[9]

Shyan-Shiou Chen, Chih-Wen Shih. Asymptotic behaviors in a transiently chaotic neural network. Discrete & Continuous Dynamical Systems - A, 2004, 10 (3) : 805-826. doi: 10.3934/dcds.2004.10.805

[10]

King Hann Lim, Hong Hui Tan, Hendra G. Harno. Approximate greatest descent in neural network optimization. Numerical Algebra, Control & Optimization, 2018, 8 (3) : 327-336. doi: 10.3934/naco.2018021

[11]

Hui-Qiang Ma, Nan-Jing Huang. Neural network smoothing approximation method for stochastic variational inequality problems. Journal of Industrial & Management Optimization, 2015, 11 (2) : 645-660. doi: 10.3934/jimo.2015.11.645

[12]

Yixin Guo, Aijun Zhang. Existence and nonexistence of traveling pulses in a lateral inhibition neural network. Discrete & Continuous Dynamical Systems - B, 2016, 21 (6) : 1729-1755. doi: 10.3934/dcdsb.2016020

[13]

Jianhong Wu, Ruyuan Zhang. A simple delayed neural network with large capacity for associative memory. Discrete & Continuous Dynamical Systems - B, 2004, 4 (3) : 851-863. doi: 10.3934/dcdsb.2004.4.851

[14]

Sanjay K. Mazumdar, Cheng-Chew Lim. A neural network based anti-skid brake system. Discrete & Continuous Dynamical Systems - A, 1999, 5 (2) : 321-338. doi: 10.3934/dcds.1999.5.321

[15]

K. L. Mak, J. G. Peng, Z. B. Xu, K. F. C. Yiu. A novel neural network for associative memory via dynamical systems. Discrete & Continuous Dynamical Systems - B, 2006, 6 (3) : 573-590. doi: 10.3934/dcdsb.2006.6.573

[16]

Rui Hu, Yuan Yuan. Stability, bifurcation analysis in a neural network model with delay and diffusion. Conference Publications, 2009, 2009 (Special) : 367-376. doi: 10.3934/proc.2009.2009.367

[17]

Feyza Gürbüz, Panos M. Pardalos. A decision making process application for the slurry production in ceramics via fuzzy cluster and data mining. Journal of Industrial & Management Optimization, 2012, 8 (2) : 285-297. doi: 10.3934/jimo.2012.8.285

[18]

Michele La Rocca, Cira Perna. Designing neural networks for modeling biological data: A statistical perspective. Mathematical Biosciences & Engineering, 2014, 11 (2) : 331-342. doi: 10.3934/mbe.2014.11.331

[19]

Liu Hui, Lin Zhi, Waqas Ahmad. Network(graph) data research in the coordinate system. Mathematical Foundations of Computing, 2018, 1 (1) : 1-10. doi: 10.3934/mfc.2018001

[20]

C. Xiong, J.P. Miller, F. Gao, Y. Yan, J.C. Morris. Testing increasing hazard rate for the progression time of dementia. Discrete & Continuous Dynamical Systems - B, 2004, 4 (3) : 813-821. doi: 10.3934/dcdsb.2004.4.813

 Impact Factor: 

Metrics

  • PDF downloads (36)
  • HTML views (432)
  • Cited by (0)

[Back to Top]