{"title":"基于轻度认知障碍筛查测试的机器学习算法。","authors":"Jin-Hyuck Park","doi":"10.1177/1533317520927163","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The mobile screening test system for mild cognitive impairment (mSTS-MCI) was developed and validated to address the low sensitivity and specificity of the Montreal Cognitive Assessment (MoCA) widely used clinically.</p><p><strong>Objective: </strong>This study was to evaluate the efficacy machine learning algorithms based on the mSTS-MCI and Korean version of MoCA.</p><p><strong>Method: </strong>In total, 103 healthy individuals and 74 patients with MCI were randomly divided into training and test data sets, respectively. The algorithm using TensorFlow was trained based on the training data set, and then its accuracy was calculated based on the test data set. The cost was calculated via logistic regression in this case.</p><p><strong>Result: </strong>Predictive power of the algorithms was higher than those of the original tests. In particular, the algorithm based on the mSTS-MCI showed the highest positive-predictive value.</p><p><strong>Conclusion: </strong>The machine learning algorithms predicting MCI showed the comparable findings with the conventional screening tools.</p>","PeriodicalId":50816,"journal":{"name":"American Journal of Alzheimers Disease and Other Dementias","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10623967/pdf/","citationCount":"0","resultStr":"{\"title\":\"Machine-Learning Algorithms Based on Screening Tests for Mild Cognitive Impairment.\",\"authors\":\"Jin-Hyuck Park\",\"doi\":\"10.1177/1533317520927163\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>The mobile screening test system for mild cognitive impairment (mSTS-MCI) was developed and validated to address the low sensitivity and specificity of the Montreal Cognitive Assessment (MoCA) widely used clinically.</p><p><strong>Objective: </strong>This study was to evaluate the efficacy machine learning algorithms based on the mSTS-MCI and Korean version of MoCA.</p><p><strong>Method: </strong>In total, 103 healthy individuals and 74 patients with MCI were randomly divided into training and test data sets, respectively. The algorithm using TensorFlow was trained based on the training data set, and then its accuracy was calculated based on the test data set. The cost was calculated via logistic regression in this case.</p><p><strong>Result: </strong>Predictive power of the algorithms was higher than those of the original tests. In particular, the algorithm based on the mSTS-MCI showed the highest positive-predictive value.</p><p><strong>Conclusion: </strong>The machine learning algorithms predicting MCI showed the comparable findings with the conventional screening tools.</p>\",\"PeriodicalId\":50816,\"journal\":{\"name\":\"American Journal of Alzheimers Disease and Other Dementias\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2020-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10623967/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"American Journal of Alzheimers Disease and Other Dementias\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/1533317520927163\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"American Journal of Alzheimers Disease and Other Dementias","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/1533317520927163","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Machine-Learning Algorithms Based on Screening Tests for Mild Cognitive Impairment.
Background: The mobile screening test system for mild cognitive impairment (mSTS-MCI) was developed and validated to address the low sensitivity and specificity of the Montreal Cognitive Assessment (MoCA) widely used clinically.
Objective: This study was to evaluate the efficacy machine learning algorithms based on the mSTS-MCI and Korean version of MoCA.
Method: In total, 103 healthy individuals and 74 patients with MCI were randomly divided into training and test data sets, respectively. The algorithm using TensorFlow was trained based on the training data set, and then its accuracy was calculated based on the test data set. The cost was calculated via logistic regression in this case.
Result: Predictive power of the algorithms was higher than those of the original tests. In particular, the algorithm based on the mSTS-MCI showed the highest positive-predictive value.
Conclusion: The machine learning algorithms predicting MCI showed the comparable findings with the conventional screening tools.
期刊介绍：
American Journal of Alzheimer''s Disease and other Dementias® (AJADD) is for professionals on the frontlines of Alzheimer''s care, dementia, and clinical depression--especially physicians, nurses, psychiatrists, administrators, and other healthcare specialists who manage patients with dementias and their families. This journal is a member of the Committee on Publication Ethics (COPE).
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