Dataset Information

Random Forest Model in the Diagnosis of Dementia Patients with Normal Mini-Mental State Examination Scores.

ABSTRACT: Background: Mini-Mental State Examination (MMSE) is the most widely used tool in cognitive screening. Some individuals with normal MMSE scores have extensive cognitive impairment. Systematic neuropsychological assessment should be performed in these patients. This study aimed to optimize the systematic neuropsychological test battery (NTB) by machine learning and develop new classification models for distinguishing mild cognitive impairment (MCI) and dementia among individuals with MMSE ≥ 26. Methods: 375 participants with MMSE ≥ 26 were assigned a diagnosis of cognitively unimpaired (CU) (n = 67), MCI (n = 174), or dementia (n = 134). We compared the performance of five machine learning algorithms, including logistic regression, decision tree, SVM, XGBoost, and random forest (RF), in identifying MCI and dementia. Results: RF performed best in identifying MCI and dementia. Six neuropsychological subtests with high-importance features were selected to form a simplified NTB, and the test time was cut in half. The AUC of the RF model was 0.89 for distinguishing MCI from CU, and 0.84 for distinguishing dementia from nondementia. Conclusions: This simplified cognitive assessment model can be useful for the diagnosis of MCI and dementia in patients with normal MMSE. It not only optimizes the content of cognitive evaluation, but also improves diagnosis and reduces missed diagnosis.

SUBMITTER: Wang J

PROVIDER: S-EPMC8780625 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Random Forest Model in the Diagnosis of Dementia Patients with Normal Mini-Mental State Examination Scores.

Wang Jie J Wang Zhuo Z Liu Ning N Liu Caiyan C Mao Chenhui C Dong Liling L Li Jie J Huang Xinying X Lei Dan D Chu Shanshan S Wang Jianyong J Gao Jing J

Journal of personalized medicine 20220104 1

<b>Background:</b> Mini-Mental State Examination (MMSE) is the most widely used tool in cognitive screening. Some individuals with normal MMSE scores have extensive cognitive impairment. Systematic neuropsychological assessment should be performed in these patients. This study aimed to optimize the systematic neuropsychological test battery (NTB) by machine learning and develop new classification models for distinguishing mild cognitive impairment (MCI) and dementia among individuals with MMSE ≥ ...[more]

PMID: 35055352

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Project description:BackgroundThe Mini-Mental State Examination-Second Edition (MMSE-2) consists of three visions: a brief version (MMSE-2:BV), a standard version (MMSE-2:SV), and an expanded version (MMSE-2: EV). Each version was equipped with alternate forms (blue and red). There was a lack of evidence on the practice effect and test-retest reliability of the three versions of the MMSE-2, limiting its utility in both clinical and research settings. The purpose of this study was to examine the practice effect and test-retest reliability of the MMSE-2 in people with dementia.MethodsOne hundred and twenty participants were enrolled, of which 60 were administered with the blue form twice (i.e., the same-form group, [SF group]) and 60 were administered with the blue form first and then the red form (alternate-form group, [AF group]). The practice effect was evaluated using a paired t-test and Cohen's d. The test-retest reliability was examined using the intraclass correlation coefficient (ICC).ResultsFor the practice effects, in the SF group, no statistically significant differences were found for the MMSE-2:BV and MMSE-2: EV total scores and eight subtests (p = 0.061-1.000), except for the MMSE-2:SV total score (p = 0.029). In the AF group, no statistically significant differences were found for all three versions of the total scores and subtests (p = 0.106-1.000), except for the visual-constructional ability subtest (p = 0.010). Cohen's d of all three versions' total scores and subtests were 0.00-0.20 and 0.00-0.26 for SF group and AF group, respectively. For the test-retest reliability, ICC values for all three versions and eight subtests in SF and AF groups were 0.60-0.93 and 0.56-0.93, respectively.ConclusionOur results demonstrated that the practice effect could be minimized when alternate forms of the MMSE-2 were used. The MMSE-2 had good to excellent test-retest reliability, except for three subtests (i.e., visual-constructional ability, registration, and recall). Caution should be taken when interpreting the results of visual-constructional ability, registration, and recall subtests of the MMSE-2.

Project description:BackgroundDisease-modifying treatments for Alzheimer's disease highlight the need for early detection of cognitive decline. However, at present, most primary care providers do not perform routine cognitive testing, in part due to a lack of access to practical cognitive assessments, as well as time and resources to administer and interpret the tests. Brief and sensitive digital cognitive assessments, such as the Digital Clock and Recall (DCR™), have the potential to address this need. Here, we examine the advantages of DCR over the Mini-Mental State Examination (MMSE) in detecting mild cognitive impairment (MCI) and mild dementia.MethodsWe studied 706 participants from the multisite Bio-Hermes study (age mean ± SD = 71.5 ± 6.7; 58.9% female; years of education mean ± SD = 15.4 ± 2.7; primary language English), classified as cognitively unimpaired (CU; n = 360), mild cognitive impairment (MCI; n = 234), or probable mild Alzheimer's dementia (pAD; n = 111) based on a review of medical history with selected cognitive and imaging tests. We evaluated cognitive classifications (MCI and early dementia) based on the DCR and the MMSE against cohorts based on the results of the Rey Auditory Verbal Learning Test (RAVLT), the Trail Making Test-Part B (TMT-B), and the Functional Activities Questionnaire (FAQ). We also compared the influence of demographic variables such as race (White vs. Non-White), ethnicity (Hispanic vs. Non-Hispanic), and level of education (≥ 15 years vs. < 15 years) on the DCR and MMSE scores.ResultsThe DCR was superior on average to the MMSE in classifying mild cognitive impairment and early dementia, AUC = 0.70 for the DCR vs. 0.63 for the MMSE. DCR administration was also significantly faster (completed in less than 3 min regardless of cognitive status and age). Among 104 individuals who were labeled as "cognitively unimpaired" by the MMSE (score ≥ 28) but actually had verbal memory impairment as confirmed by the RAVLT, the DCR identified 84 (80.7%) as impaired. Moreover, the DCR score was significantly less biased by ethnicity than the MMSE, with no significant difference in the DCR score between Hispanic and non-Hispanic individuals.ConclusionsDCR outperforms the MMSE in detecting and classifying cognitive impairment-in a fraction of the time-while being not influenced by a patient's ethnicity. The results support the utility of DCR as a sensitive and efficient cognitive assessment in primary care settings.Trial registrationClinicalTrials.gov identifier NCT04733989.

Dataset Information

Random Forest Model in the Diagnosis of Dementia Patients with Normal Mini-Mental State Examination Scores.

Publications

Random Forest Model in the Diagnosis of Dementia Patients with Normal Mini-Mental State Examination Scores.

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