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Diagnosis of Dementia by Machine learning methods in Epidemiological studies: a pilot exploratory study from south India

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Abstract

Background

There are limited data on the use of artificial intelligence methods for the diagnosis of dementia in epidemiological studies in low- and middle-income country (LMIC) settings. A culture and education fair battery of cognitive tests was developed and validated for population based studies in low- and middle-income countries including India by the 10/66 Dementia Research Group.

Aims

We explored the machine learning methods based on the 10/66 battery of cognitive tests for the diagnosis of dementia based in a birth cohort study in South India.

Methods

The data sets for 466 men and women for this study were obtained from the on-going Mysore Studies of Natal effect of Health and Ageing (MYNAH), in south India. The data sets included: demographics, performance on the 10/66 cognitive function tests, the 10/66 diagnosis of mental disorders and population based normative data for the 10/66 battery of cognitive function tests. Diagnosis of dementia from the rule based approach was compared against the 10/66 diagnosis of dementia. We have applied machine learning techniques to identify minimal number of the 10/66 cognitive function tests required for diagnosing dementia and derived an algorithm to improve the accuracy of dementia diagnosis.

Results

Of 466 subjects, 27 had 10/66 diagnosis of dementia, 19 of whom were correctly identified as having dementia by Jrip classification with 100% accuracy.

Conclusions

This pilot exploratory study indicates that machine learning methods can help identify community dwelling older adults with 10/66 criterion diagnosis of dementia with good accuracy in a LMIC setting such as India. This should reduce the duration of the diagnostic assessment and make the process easier and quicker for clinicians, patients and will be useful for ‘case’ ascertainment in population based epidemiological studies.

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Abbreviations

CSI ‘D’:

Community screening instrument for dementia

CSID ‘I’:

Community screening instrument for dementia-informant interview

COAD:

Chronic obstructive airway disease

VF:

Verbal fluency

WLR:

Word list recall

CERAD:

The consortium to establish a registry for Alzheimer’s disease

TP:

True positive

TN:

True negative

FP:

False positive

FN:

False negative

LMIC:

Low- and middle-income country

MMSE:

Mini mental state examination

BOMC:

Blessed orientation memory and concentration test

MOCA:

Montreal cognitive assessment

AD8:

Ascertain dementia 8

GP CoG:

General practitioner assessment of cognition

IB1:

Instance-based learning version 1

ARFF:

Attribute-relation File Format

CSV:

Comma separated Variable

References

  1. Edition F (1994) Diagnostic and statistical manual of mental disorders. American Psychological Association, Washington

  2. Datta P, Shankle W, Pazzani M (1996) Applying machine learning to an Alzheimer’s database. Paper presented at the conference proceedings of the AAAI symposium

  3. Ganguli M, Chandra V, Gilby JE, Ratcliff G, Sharma SD, Pandav R, Belle S (1996) Cognitive test performance in a community-based nondemented elderly sample in rural India: the Indo-US cross-national dementia epidemiology study. Int Psychogeriatr 8(04):507–524

    Article  CAS  PubMed  Google Scholar 

  4. Guruje O, Unverzargt F, Osuntokun B, Hendrie H, Baiyewu O, Ogunniyi A, Hali K (1994) The CERAD neuropsychological test battery: norms from a yoruba-speaking Nigerian sample. West Afr J Med 14(1):29–33

    Google Scholar 

  5. Hall KS, Gao S, Emsley CL, Ogunniyi AO, Morgan O, Hendrie HC (2000) Community screening interview for dementia (CSI ‘D’); performance in five disparate study sites. Int J Geriat Psychiatry 15(6):521–531

    Article  CAS  Google Scholar 

  6. Han J, Kamber M, Pei J (2012) 2012 data mining. Concepts and techniques, pdf

  7. Hendrie HC, Hall KS, Pillay N, Rodgers D, Prince C, Norton J, Kaufert J (1993) Alzheimer’s disease is rare in Cree. Int Psychogeriatr 5(01):5–14

    Article  CAS  PubMed  Google Scholar 

  8. Hendrie HC, Osuntokun BO, Hall KS, Ogunniyi AO (1995) Prevalence of Alzheimer’s disease and dementia in two communities: Nigerian Africans and African Americans. Am J Psychiatry 152(10):1485

    Article  CAS  PubMed  Google Scholar 

  9. Herron P (2004) Machine learning for medical decision support: evaluating diagnostic performance of machine learning classification algorithms. Data Mining Spring, New York, p 24

    Google Scholar 

  10. Joshi S, Shenoy PD, Venugopal K, Patnaik L (2009) Evaluation of different stages of dementia employing neuropsychological and machine learning techniques. Paper presented at the Advanced Computing, 2009. ICAC 2009. First International Conference on

  11. Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97(1–2):273–324

    Article  Google Scholar 

  12. Krishna M, Beulah E, Jones S, Sundarachari R, A S, Kumaran K, Karat SC, Copeland JR, Prince M, Fall C (2016) Cognitive function and disability in late life: an ecological validation of the 10/66 battery of cognitive tests among community‐dwelling older adults in South India. Int J Geriat Psychiatry 31(8):879–891

    Article  Google Scholar 

  13. Krishna M, Kalyanaraman K, Veena S, Krishanveni G, Karat S, Cox V, Paul B (2015) Cohort profile: the 1934–66 Mysore birth records cohort in South India. Int J Epidemiol, dyv176

  14. Maestre GE (2012) Assessing dementia in resource-poor regions. Curr Neurol Neurosci Reports 12(5):511–519

    Article  Google Scholar 

  15. Moms J, Heyman A, Mohs R, Hughes JP, Van Belle G, Fillenbaum G, Clark C (1989) The consortium to establish a Registry for Alzheimer’s Disease (CERAD). Part I. Clinical and neuropsychological assessment of Alzheimer’s disease. Neurology 39(9):1159

    Article  Google Scholar 

  16. Prince M, Acosta D, Chiu H, Scazufca M, Varghese M, Group DR (2003) Dementia diagnosis in developing countries: a cross-cultural validation study. Lancet 361(9361):909–917

    Article  Google Scholar 

  17. Prince M, Bryce R, Ferri C (2014) The World Alzheimer report 2011: the benefits of early diagnosis and intervention. 2011. London: Alzheimer’s Disease Int Google Scholar

  18. Prince M, Ferri CP, Acosta D, Albanese E, Arizaga R, Dewey M, Jacob K (2007) The protocols for the 10/66 dementia research group population-based research programme. BMC Public Health 7(1):165

    Article  PubMed  PubMed Central  Google Scholar 

  19. Schmitter-Edgecombe M, Parsey C, Cook DJ (2011) Cognitive correlates of functional performance in older adults: comparison of self-report, direct observation, and performance-based measures. J Int Neuropsychol Soc 17(05):853–864

    Article  PubMed  PubMed Central  Google Scholar 

  20. Shaji K, Jotheeswaran A, Girish N, Srikala B, Dias A, Pattabiraman M, Varghese M (2010) The dementia India report: prevalence, impact, costs and services for dementia: executive summary. Alzheimer’s Related Disorders Society of India, pp 1–38

  21. Shankle WR, Datta P, Dillencourt M, Pazzani M (1996) Improving dementia screening tests with machine learning methods. Alzheimer’s Res 2(3)

  22. Shree SB, Sheshadri HS (2014) An approach to preprocess data in the diagnosis of Alzheimer’s disease. In: Cloud computing and internet of things (CCIOT), 2014 International conference on. IEEE, pp 135–139. http://ieeexplore.ieee.org/search/searchresult.jsp?queryText=An%20approach%20to%20preprocess%20data%20in%20the%20diagnosis%20of%20Alzheimer%E2%80%99s%20disease.%20In:%20Cloud%20computing%20and%20internet%20of%20things%20.LB.CCIOT.RB.,%202014%20International%20Conference%20o&newsearch=true

  23. Sheshadri H, Shree SB, Krishna M (2015) Diagnosis of Alzheimer’s disease employing neuropsychological and classification techniques. In: IT convergence and security (ICITCS), 2015 5th International conference on. IEEE, pp 1–6

  24. Shree SB, Sheshadri H (2014) An initial investigation in the diagnosis of Alzheimer’s disease using various classification techniques. In: Computational intelligence and computing research (ICCIC), 2014 IEEE international conference on. IEEE, pp 1–5

  25. Soman K, Diwakar S, Ajay V (2006) Data mining: theory and practice [WITH CD]: PHI Learning Pvt. Ltd

  26. Sosa AL, Albanese E, Prince M, Acosta D, Ferri CP, Guerra M, Salas A (2009) Population normative data for the 10/66 Dementia Research Group cognitive test battery from Latin America, India and China: a cross-sectional survey. BMC Neurol 9(1):48

    Article  PubMed  PubMed Central  Google Scholar 

  27. Unverzagt FW, Morgan OS, Thesiger CH, Eldemire DA, Luseko J, Pokuri S, Hendrie HC (1999) Clinical utility of CERAD neuropsychological battery in elderly Jamaicans. J Int Neuropsychol Soc 5(03):255–259

    Article  CAS  PubMed  Google Scholar 

  28. Welsh K, Butters N, Hughes J, Mohs R, Heyman A (1991) Detection of abnormal memory decline in mild cases of Alzheimer’s disease using CERAD neuropsychological measures. Arch Neurol 48(3):278–281

    Article  CAS  PubMed  Google Scholar 

  29. Williams JA, Weakley A, Cook DJ, Schmitter-Edgecombe M (2013) Machine learning techniques for diagnostic differentiation of mild cognitive impairment and dementia. Paper presented at the Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence

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Acknowledgements

This study was partly funded by the Wellcome DBT India Alliance as an Early Career Research Fellowship to Dr Murali Krishna. The grant providers did not influence the design, conduct of the study and interpretation of the findings. Our sincere thanks to the participants and their families for taking part in this study. We thank Saroja A, Santhosh Nagaraj, Ramya R and Bhavya Hegde, CSI Holdsworth Memorial Hospital for assistance with recruitment and assessments this study. We thank management, ATME College of Engineering, Mysore for the support. We thank MS Patsy Coakley, MRC Life course Epidemiology Unit for their support with data management.

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Authors and Affiliations

  1. E&C Department, ATME College of Engineering, Mysore, India

    Sheshadri Iyengar Raghavan Bhagyashree

  2. CSI Holdsworth Memorial Hospital, PO Box 28, Mandimohalla, Mysore, India

    Kiran Nagaraj & Murali Krishna

  3. Department of Epidemiological Psychiatry, Institute of Psychiatry, Kings College, London, UK

    Martin Prince

  4. International Paediatric Endocrinology, MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK

    Caroline H. D. Fall

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  1. Sheshadri Iyengar Raghavan Bhagyashree
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  2. Kiran Nagaraj
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  3. Martin Prince
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  4. Caroline H. D. Fall
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  5. Murali Krishna
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Correspondence to Sheshadri Iyengar Raghavan Bhagyashree.

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Bhagyashree, S.R., Nagaraj, K., Prince, M. et al. Diagnosis of Dementia by Machine learning methods in Epidemiological studies: a pilot exploratory study from south India. Soc Psychiatry Psychiatr Epidemiol 53, 77–86 (2018). https://doi.org/10.1007/s00127-017-1410-0

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  • DOI: https://doi.org/10.1007/s00127-017-1410-0

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