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      Dementia risks identified by vocal features via telephone conversations: A novel machine learning prediction model

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          Abstract

          Due to difficulty in early diagnosis of Alzheimer’s disease (AD) related to cost and differentiated capability, it is necessary to identify low-cost, accessible, and reliable tools for identifying AD risk in the preclinical stage. We hypothesized that cognitive ability, as expressed in the vocal features in daily conversation, is associated with AD progression. Thus, we have developed a novel machine learning prediction model to identify AD risk by using the rich voice data collected from daily conversations, and evaluated its predictive performance in comparison with a classification method based on the Japanese version of the Telephone Interview for Cognitive Status (TICS-J). We used 1,465 audio data files from 99 Healthy controls (HC) and 151 audio data files recorded from 24 AD patients derived from a dementia prevention program conducted by Hachioji City, Tokyo, between March and May 2020. After extracting vocal features from each audio file, we developed machine-learning models based on extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR), using each audio file as one observation. We evaluated the predictive performance of the developed models by describing the receiver operating characteristic (ROC) curve, calculating the areas under the curve (AUCs), sensitivity, and specificity. Further, we conducted classifications by considering each participant as one observation, computing the average of their audio files’ predictive value, and making comparisons with the predictive performance of the TICS-J based questionnaire. Of 1,616 audio files in total, 1,308 (81.0%) were randomly allocated to the training data and 308 (19.1%) to the validation data. For audio file-based prediction, the AUCs for XGboost, RF, and LR were 0.863 (95% confidence interval [CI]: 0.794–0.931), 0.882 (95% CI: 0.840–0.924), and 0.893 (95%CI: 0.832–0.954), respectively. For participant-based prediction, the AUC for XGboost, RF, LR, and TICS-J were 1.000 (95%CI: 1.000–1.000), 1.000 (95%CI: 1.000–1.000), 0.972 (95%CI: 0.918–1.000) and 0.917 (95%CI: 0.918–1.000), respectively. There was difference in predictive accuracy of XGBoost and TICS-J with almost approached significance (p = 0.065). Our novel prediction model using the vocal features of daily conversations demonstrated the potential to be useful for the AD risk assessment.

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          Most cited references47

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          Diagnostic and Statistical Manual of Mental Disorders

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            Random Forests

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              Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draft
                Role: ConceptualizationRole: Data curationRole: MethodologyRole: ResourcesRole: SoftwareRole: Writing – original draft
                Role: SupervisionRole: Writing – review & editing
                Role: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                14 July 2021
                2021
                : 16
                : 7
                : e0253988
                Affiliations
                [1 ] Department of Public Health, McCann Healthcare Worldwide Japan Inc., Tokyo, Japan
                [2 ] Department of Global Health Promotion, Tokyo Medical and Dental University, Tokyo, Japan
                [3 ] Graduate School of Health Management, Keio University, Tokyo, Japan
                [4 ] Department of Social Epidemiology and Global Health, Graduate School of Medicine and School of Public Health, Kyoto University, Kyoto, Japan
                Vellore Institute of Technology: VIT University, INDIA
                Author notes

                Competing Interests: AS, YI, and HH are employees of McCann Health Worldwide Japan Inc. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

                Author information
                https://orcid.org/0000-0002-5116-3788
                Article
                PONE-D-21-04453
                10.1371/journal.pone.0253988
                8279312
                34260593
                b4b42225-6b32-4903-88c0-afe2732b8fa4
                © 2021 Shimoda et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 9 February 2021
                : 16 June 2021
                Page count
                Figures: 3, Tables: 4, Pages: 15
                Funding
                This research was funded by McCann Health Worldwide Japan Inc. The funder provided support in the form of salaries for AS, YI, and HH, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.
                Categories
                Research Article
                Medicine and Health Sciences
                Mental Health and Psychiatry
                Dementia
                Alzheimer's Disease
                Medicine and Health Sciences
                Neurology
                Dementia
                Alzheimer's Disease
                Medicine and Health Sciences
                Medical Conditions
                Neurodegenerative Diseases
                Alzheimer's Disease
                Medicine and Health Sciences
                Neurology
                Neurodegenerative Diseases
                Alzheimer's Disease
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Forecasting
                Physical Sciences
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                Computer and Information Sciences
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                Epidemiology
                Medical Risk Factors
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                Artificial Intelligence
                Machine Learning
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                Biology and Life Sciences
                Organisms
                Eukaryota
                Plants
                Trees
                Medicine and Health Sciences
                Mental Health and Psychiatry
                Dementia
                Medicine and Health Sciences
                Neurology
                Dementia
                Custom metadata
                The data for the current research are copyright to Softfront Japan, Inc., Tokyo, Japan. Data cannot be shared publicly because the data are owned by a third party and the authors do not have permission to share the data. Data can be requested from the company (contact via sales@ 123456softfront-japan.co.jp ) for researchers who meet the criteria for access to confidential data. The authors had no privileged access to the data. Other researchers can access as per request from the company.

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