Abstract
BackgroundThe Harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD) is the first and only nationally representative study on late-life cognition and dementia in India (n=4096). LASI-DAD obtained clinical consensus diagnosis of dementia for a subsample of 2528 respondents.ObjectiveThis study develops a machine learning model that uses data from the clinical consensus diagnosis in LASI-DAD to support the classification of dementia status.MethodsClinicians were presented with the extensive data collected from LASI-DAD, including sociodemographic information and health history of respondents, results from the screening tests of cognitive status, and information obtained from informant interviews. Based on the Clinical Dementia Rating (CDR) and using an online platform, clinicians individually evaluated each case and then reached a consensus diagnosis. A 2-step procedure was implemented to train several candidate machine learning models, which were evaluated using a separate test set for predictive accuracy measurement, including the area under receiver operating curve (AUROC), accuracy, sensitivity, specificity, precision, F1 score, and kappa statistic. The ultimate model was selected based on overall agreement as measured by kappa. We further examined the overall accuracy and agreement with the final consensus diagnoses between the selected machine learning model and individual clinicians who participated in the clinical consensus diagnostic process. Finally, we applied the selected model to a subgroup of LASI-DAD participants for whom the clinical consensus diagnosis was not obtained to predict their dementia status.ResultsAmong the 2528 individuals who received clinical consensus diagnosis, 192 (6.7% after adjusting for sampling weight) were diagnosed with dementia. All candidate machine learning models achieved outstanding discriminative ability, as indicated by AUROC >.90, and had similar accuracy and specificity (both around 0.95). The support vector machine model outperformed other models with the highest sensitivity (0.81), F1 score (0.72), and kappa (.70, indicating substantial agreement) and the second highest precision (0.65). As a result, the support vector machine was selected as the ultimate model. Further examination revealed that overall accuracy and agreement were similar between the selected model and individual clinicians. Application of the prediction model on 1568 individuals without clinical consensus diagnosis classified 127 individuals as living with dementia. After applying sampling weight, we can estimate the prevalence of dementia in the population as 7.4%.ConclusionsThe selected machine learning model has outstanding discriminative ability and substantial agreement with a clinical consensus diagnosis of dementia. The model can serve as a computer model of the clinical knowledge and experience encoded in the clinical consensus diagnostic process and has many potential applications, including predicting missed dementia diagnoses and serving as a clinical decision support tool or virtual rater to assist diagnosis of dementia.
Highlights
The World Health Organization estimates that the number of people living with dementia worldwide is approximately 50 million and will almost triple by 2050 [1], with nearly 60% living in low- and middle-income countries like India [2]
The objective of this study is to develop a machine learning model that uses information from the clinical consensus diagnosis in the Longitudinal Aging Study in India (LASI)-DAD for classification of dementia
This study developed a machine learning model using the same predictors as the LASI-DAD assessment and informant interview data in the clinical consensus diagnosis
Summary
The World Health Organization estimates that the number of people living with dementia worldwide is approximately 50 million and will almost triple by 2050 [1], with nearly 60% living in low- and middle-income countries like India [2]. Developing effective population-based interventions to address the rising burden of dementia depends on high-quality nationally representative data, which is often scarce in low- and middle-income countries. The Alzheimer’s and Related Disorders Society of India estimates that more than 3.7 million Indians have dementia This figure is based on a meta-analysis of prevalence studies with estimated prevalence rates ranging from 0.6% to 10.6% in rural areas and from 0.9% to 7.5% in urban areas [3,4]. The Harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD) further extends the LASI’s cognitive data collection by conducting in-depth neuropsychological tests and informant interviews for a subsample of the LASI respondents aged 60 years and older [3]. LASI-DAD obtained clinical consensus diagnosis of dementia for a subsample of 2528 respondents
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.