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Application of machine learning methods for diagnosis of dementia based on the 10/66 battery of cognitive function tests in South India

Application of machine learning methods for diagnosis of dementia based on the 10/66 battery of cognitive function tests in South India
Application of machine learning methods for diagnosis of dementia based on the 10/66 battery of cognitive function tests in South India
Background: There is limited data on the use of Machine learning methods for automating clinical aspects of dementia in low and middle income country (LMIC) settings including India. A culture and education fair battery of cognitive tests was developed, validated and normed for use in LMICs including south India by the 10/66 Dementia Research Group. We explored the machine learning algorithms to determine if the analysis of neuropsychological data from the 10/66 battery of cognitive tests can be automated for the diagnosis of dementia in south India.

Methods: The data sets for 466 men and women aged 55- 80 yrs were obtained from the on-going Mysore Studies of Natal effect of Health and Ageing (MYNAH), in south India. This includes subject demographics, performance on the 10/66 cognitive function tests, diagnosis of mental disorders and population based normative data for the 10/66 battery of cognitive function tests. We examined the diagnostic properties of the battery of cognitive tests and derived an equation to enhance the accuracy of diagnosis of dementia. Machine learning techniques were applied to the data set.

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

Conclusions: This pilot exploratory study indicates that machine learning methods can help to identify community dwelling older adults with 10/66 criterion diagnosis of dementia with good accuracy in a LMIC setting like India. This should reduce the duration of the diagnostic assessment and make the process easier and quicker for both the clinicians and patients.
Bhagyashree, S.R.
5dfffbcc-e175-48d4-a904-b6ff29d10b07
Sheshadri, H.S.
67e4acfe-ae0f-4ab8-bf62-26b40f1294c1
Nagaraj, Kiran
cdfac14e-6c86-4648-b2a2-90f18d84ff69
Prince, Martin
ff242776-7147-4b2a-96ac-05f2c8f008e3
Fall, Caroline
7171a105-34f5-4131-89d7-1aa639893b18
Krishna, Murali
323774b9-e195-4608-8aa2-adb3d16f4637
Bhagyashree, S.R.
5dfffbcc-e175-48d4-a904-b6ff29d10b07
Sheshadri, H.S.
67e4acfe-ae0f-4ab8-bf62-26b40f1294c1
Nagaraj, Kiran
cdfac14e-6c86-4648-b2a2-90f18d84ff69
Prince, Martin
ff242776-7147-4b2a-96ac-05f2c8f008e3
Fall, Caroline
7171a105-34f5-4131-89d7-1aa639893b18
Krishna, Murali
323774b9-e195-4608-8aa2-adb3d16f4637

Bhagyashree, S.R., Sheshadri, H.S., Nagaraj, Kiran, Prince, Martin, Fall, Caroline and Krishna, Murali (2017) Application of machine learning methods for diagnosis of dementia based on the 10/66 battery of cognitive function tests in South India. Social Psychiatry and Psychiatric Epidemiology. (In Press)

Record type: Article

Abstract

Background: There is limited data on the use of Machine learning methods for automating clinical aspects of dementia in low and middle income country (LMIC) settings including India. A culture and education fair battery of cognitive tests was developed, validated and normed for use in LMICs including south India by the 10/66 Dementia Research Group. We explored the machine learning algorithms to determine if the analysis of neuropsychological data from the 10/66 battery of cognitive tests can be automated for the diagnosis of dementia in south India.

Methods: The data sets for 466 men and women aged 55- 80 yrs were obtained from the on-going Mysore Studies of Natal effect of Health and Ageing (MYNAH), in south India. This includes subject demographics, performance on the 10/66 cognitive function tests, diagnosis of mental disorders and population based normative data for the 10/66 battery of cognitive function tests. We examined the diagnostic properties of the battery of cognitive tests and derived an equation to enhance the accuracy of diagnosis of dementia. Machine learning techniques were applied to the data set.

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

Conclusions: This pilot exploratory study indicates that machine learning methods can help to identify community dwelling older adults with 10/66 criterion diagnosis of dementia with good accuracy in a LMIC setting like India. This should reduce the duration of the diagnostic assessment and make the process easier and quicker for both the clinicians and patients.

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Accepted/In Press date: 15 June 2017

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Local EPrints ID: 424558
URI: http://eprints.soton.ac.uk/id/eprint/424558
PURE UUID: ccf57210-322f-4068-b587-c7eeaac6dcff

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Date deposited: 05 Oct 2018 11:38
Last modified: 08 May 2020 04:01

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