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

Wang, Jie; Wang, Zhuo; Liu, Ning; Liu, Caiyan; Mao, Chenhui; Dong, Liling; Li, Jie; Huang, Xin; Lei, Dan; Chu, Shanshan; Wang, Jianyong; Gao, Jing

doi:10.3390/jpm12010037

Cited by 13 publications

(8 citation statements)

References 47 publications

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“…The RF method is one of the popular and preferable methods used by researchers. It has proven to be more effective in dementia prediction 40 – 44 than other ML methods and is not hindered by the “black box” performance of certain ML approaches. The RF method can not only be used to predict results but also to show the importance of features used in prediction.…”

Section: Introductionmentioning

confidence: 99%

Long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning methods

Yan

Chen

Yang

et al. 2022

Sci Rep

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Air pollution exposure has been linked to various diseases, including dementia. However, a novel method for investigating the associations between air pollution exposure and disease is lacking. The objective of this study was to investigate whether long-term exposure to ambient particulate air pollution increases dementia risk using both the traditional Cox model approach and a novel machine learning (ML) with random forest (RF) method. We used health data from a national population-based cohort in Taiwan from 2000 to 2017. We collected the following ambient air pollution data from the Taiwan Environmental Protection Administration (EPA): fine particulate matter (PM2.5) and gaseous pollutants, including sulfur dioxide (SO2), carbon monoxide (CO), ozone (O3), nitrogen oxide (NOx), nitric oxide (NO), and nitrogen dioxide (NO2). Spatiotemporal-estimated air quality data calculated based on a geostatistical approach, namely, the Bayesian maximum entropy method, were collected. Each subject's residential county and township were reviewed monthly and linked to air quality data based on the corresponding township and month of the year for each subject. The Cox model approach and the ML with RF method were used. Increasing the concentration of PM2.5 by one interquartile range (IQR) increased the risk of dementia by approximately 5% (HR = 1.05 with 95% CI = 1.04–1.05). The comparison of the performance of the extended Cox model approach with the RF method showed that the prediction accuracy was approximately 0.7 by the RF method, but the AUC was lower than that of the Cox model approach. This national cohort study over an 18-year period provides supporting evidence that long-term particulate air pollution exposure is associated with increased dementia risk in Taiwan. The ML with RF method appears to be an acceptable approach for exploring associations between air pollutant exposure and disease.

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Section: Introductionmentioning

confidence: 99%

Long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning methods

Yan

Chen

Yang

et al. 2022

Sci Rep

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“…So far, a lot of studies have focused on the classification of NC and MCI by using machine learning models for screening in primary care. For instance, Siuly et al performed a Piecewise Aggregate Approximation (PAA) technique for compressing massive volumes of EEG data for reliable analysis and developed a model based on Extreme Learning Machine (ELM) with permutation entropy (PE) and auto-regressive (AR) model features to achieve the highest MCI classification accuracy (98.8%) [ 33 ]; Lagun et al applied a SVM based machine learning model to reach the accuracy of 87% to detect MCI by modeling eye movement characteristics such as fixations, saccades, and refixations during the Visual Paired Comparison (VPC) task [ 34 ]; Yim et al developed a screening model based on a gradient boosting (GB) algorithm to identify MCI by neuropsychological test results and reached the classification accuracy of 93.5% [ 15 ]; and, Wang et al developed a Random Forest (RF)-based model to optimize the content of cognitive evaluation and achieved an accuracy of 68% in the classification of MCI and NC [ 35 ].…”

Section: Discussionmentioning

confidence: 99%

Development of a Machine Learning Model to Discriminate Mild Cognitive Impairment Subjects from Normal Controls in Community Screening

Jiang

Zhang

et al. 2022

Brain Sciences

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Background: Mild cognitive impairment (MCI) is a transitional stage between normal aging and probable Alzheimer’s disease. It is of great value to screen for MCI in the community. A novel machine learning (ML) model is composed of electroencephalography (EEG), eye tracking (ET), and neuropsychological assessments. This study has been proposed to identify MCI subjects from normal controls (NC). Methods: Two cohorts were used in this study. Cohort 1 as the training and validation group, includes184 MCI patients and 152 NC subjects. Cohort 2 as an independent test group, includes 44 MCI and 48 NC individuals. EEG, ET, Neuropsychological Tests Battery (NTB), and clinical variables with age, gender, educational level, MoCA-B, and ACE-R were selected for all subjects. Receiver operating characteristic (ROC) curves were adopted to evaluate the capabilities of this tool to classify MCI from NC. The clinical model, the EEG and ET model, and the neuropsychological model were compared. Results: We found that the classification accuracy of the proposed model achieved 84.5 ± 4.43% and 88.8 ± 3.59% in Cohort 1 and Cohort 2, respectively. The area under curve (AUC) of the proposed tool achieved 0.941 (0.893–0.982) in Cohort 1 and 0.966 (0.921–0.988) in Cohort 2, respectively. Conclusions: The proposed model incorporation of EEG, ET, and neuropsychological assessments yielded excellent classification performances, suggesting its potential for future application in cognitive decline prediction.

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“…The cognitive composite consisted of more than 20 neuropsychological subtests that assessed five cognitive domains, including executive function, visuospatial function, language function, memory function (verbal and nonverbal memory), and conceptual reasoning and computation. This has been explained in detail previously (Wang et al, 2022).…”

Section: Neuropsychological Assessmentmentioning

confidence: 95%

Association of APOE ε4/ε4 with fluid biomarkers in patients from the PUMCH dementia cohort

Shang

Dong

Huang

et al. 2023

Front. Aging Neurosci.

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BackgroundApolipoprotein-E (APOE) ε4 is a major genetic risk factor for Alzheimer’s disease (AD). Current studies, which were mainly based on the clinical diagnosis rather than biomarkers, come to inconsistent conclusions regarding the associations of APOE ε4 homozygotes (APOE ε4/ε4) and cerebrospinal fluid (CSF) biomarkers of AD. In addition, few studies have explored the associations of APOE ε4/ε4 with plasma biomarkers. Therefore, we aimed to investigate the associations of APOE ε4/ε4 with fluid biomarkers in dementia and biomarker-diagnosed AD.MethodsA total of 297 patients were enrolled. They were classified into Alzheimer’s continuum, AD, and non-AD, according to CSF biomarkers and/or β amyloid PET results. AD was a subgroup of the AD continuum. Plasma Amyloid β (Aβ) 40, Aβ42, glial fibrillary acidic protein (GFAP), neurofilament light chain (NFL), and phosphorylated tau (P-tau)181 were quantified in 144 of the total population using an ultra-sensitive Simoa technology. We analyzed the associations of APOE ε4/ε4 on CSF and plasma biomarkers in dementia and biomarker diagnosed AD.ResultsBased on the biomarker diagnostic criteria, 169 participants were diagnosed with Alzheimer’s continuum and 128 individuals with non-AD, and among the former, 120 patients with AD. The APOE ε4/ε4 frequencies were 11.8% (20/169), 14.2% (17/120), and 0.8% (1/128) in Alzheimer’s continuum, AD and non-AD, respectively. Only CSF Aβ42 was shown to be decreased in APOE ε4/ε4 carriers than in non-carriers for patients with AD (p = 0.024). Furthermore, we did not find any associations of APOE ε4 with plasma biomarkers of AD and non-AD. Interestingly, we found that in non-AD patients, APOE ε4 carriers had lower CSF Aβ42 (p = 0.018) and higher T-tau/Aβ42 ratios (p < 0.001) and P-tau181/Aβ42 ratios (p = 0.002) than non-carriers.ConclusionOur data confirmed that of the three groups (AD continuum, AD, and non-AD), those with AD had the highest frequency of APOE ɛ4/ɛ4 genotypes. The APOE ɛ4/ɛ4 was associated with CSF levels of Aβ42 but not tau for AD and non-AD, suggesting that APOE ɛ4/ɛ4 affected the Aβ metabolism of both. No associations between APOE ε4/ɛ4 and plasma biomarkers of AD and non-AD were found.

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

Cited by 13 publications

References 47 publications

Long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning methods

Long-term exposure to particulate matter was associated with increased dementia risk using both traditional approaches and novel machine learning methods

Development of a Machine Learning Model to Discriminate Mild Cognitive Impairment Subjects from Normal Controls in Community Screening

Association of APOE ε4/ε4 with fluid biomarkers in patients from the PUMCH dementia cohort

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