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Updated: Aug 15, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Validity of three risk prediction models for dementia or cognitive impairment in Australia
Gopisankar M Geethadevi1, Roseanne Peel2, J Simon Bell1
1Faculty of Pharmacy and Pharmaceutical Sciences, Centre for Medicine Use and Safety, Monash University, Melbourne, VIC, Australia.
Background:
no studies have compared the predictive validity of different dementia risk prediction models in Australia.
Objectives:
(i) to investigate the predictive validity of the Australian National University-Alzheimer's Disease Risk Index (ANU-ADRI), LIfestyle for BRAin Health (LIBRA) Index and cardiovascular risk factors, ageing and dementia study (CAIDE) models for predicting probable dementia/cognitive impairment in an Australian cohort. (ii) To develop and assess the predictive validity of a new hybrid model combining variables from the three models.
Methods:
the Hunter Community Study (HCS) included 3,306 adults aged 55-85 years with a median follow-up of 7.1 years. Probable dementia/cognitive impairment was defined using Admitted Patient Data Collection, dispensing of cholinesterase inhibitors or memantine, or a cognitive test. Model validity was assessed by calibration and discrimination. A hybrid model was developed using deep neural network analysis, a machine learning method.
Results:
120 (3.6%) participants developed probable dementia/cognitive impairment. Mean calibration by ANU-ADRI, LIBRA, CAIDE and the hybrid model was 19, 0.5, 4.7 and 3.4%, respectively. The discrimination of the models was 0.65 (95% CI 0.60-0.70), 0.65 (95% CI 0.60-0.71), 0.54 (95% CI 0.49-0.58) and 0.80 (95% CI 0.78-0.83), respectively.
Conclusion:
ANU-ADRI and LIBRA were better dementia prediction tools than CAIDE for identification of high-risk individuals in this cohort. ANU-ADRI overestimated and LIBRA underestimated the risk. The new hybrid model had a higher predictive performance than the other models but it needs to be validated independently in longitudinal studies.
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