scholarly journals Machine-Learning Algorithms Based on Screening Tests for Mild Cognitive Impairment

2020 ◽  
Vol 35 ◽  
pp. 153331752092716
Author(s):  
Jin-Hyuck Park
Keyword(s):  
Machine Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Test Data ◽  
Learning Algorithms ◽  
Test System ◽  
Machine Learning Algorithms ◽  
Screening Tools ◽  
Screening Tests ◽  
Data Set

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.

Plasma d-glutamate levels for detecting mild cognitive impairment and Alzheimer’s disease: Machine learning approaches

2021 ◽  
Vol 35 (3) ◽  
pp. 265-272 ◽  
Author(s):  
Chun-Hung Chang ◽  
Chieh-Hsin Lin ◽  
Chieh-Yu Liu ◽  
Chih-Sheng Huang ◽  
Shaw-Ji Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Random Forest ◽  
Naive Bayes ◽  
Mmse Score ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Healthy Controls ◽  
Peripheral Plasma

Background: d-glutamate, which is involved in N-methyl-d-aspartate receptor modulation, may be associated with cognitive ageing. Aims: This study aimed to use peripheral plasma d-glutamate levels to differentiate patients with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) from healthy individuals and to evaluate its prediction ability using machine learning. Methods: Overall, 31 healthy controls, 21 patients with MCI and 133 patients with AD were recruited. Serum d-glutamate levels were measured using high-performance liquid chromatography (HPLC). Cognitive deficit severity was assessed using the Clinical Dementia Rating scale and the Mini-Mental Status Examination (MMSE). We employed four machine learning algorithms (support vector machine, logistic regression, random forest and naïve Bayes) to build an optimal predictive model to distinguish patients with MCI or AD from healthy controls. Results: The MCI and AD groups had lower plasma d-glutamate levels (1097.79 ± 283.99 and 785.10 ± 720.06 ng/mL, respectively) compared to healthy controls (1620.08 ± 548.80 ng/mL). The naïve Bayes model and random forest model appeared to be the best models for determining MCI and AD susceptibility, respectively (area under the receiver operating characteristic curve: 0.8207 and 0.7900; sensitivity: 0.8438 and 0.6997; and specificity: 0.8158 and 0.9188, respectively). The total MMSE score was positively correlated with d-glutamate levels ( r = 0.368, p < 0.001). Multivariate regression analysis indicated that d-glutamate levels were significantly associated with the total MMSE score ( B = 0.003, 95% confidence interval 0.002–0.005, p < 0.001). Conclusions: Peripheral plasma d-glutamate levels were associated with cognitive impairment and may therefore be a suitable peripheral biomarker for detecting MCI and AD. Rapid and cost-effective HPLC for biomarkers and machine learning algorithms may assist physicians in diagnosing MCI and AD in outpatient clinics.

scholarly journals Artificial Intelligence and Machine Learning Algorithms For Informing the Diagnostic Process of Mild Cognitive Impairment and Dementia

2019 ◽  
Vol 34 (6) ◽  
pp. 838-838
Author(s):  
J Gardner
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Sensitivity And Specificity ◽  
Clinical Judgment ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Bivariate Correlation

Abstract Objective Differentiating between a clinical diagnosis of Mild Cognitive Impairment (MCI) and dementia is difficult due to expansive data needs in concert with ambiguity of clinical criteria. Novel artificial intelligence (AI) and machine learning algorithms provide potential avenues for efficiently analyzing data sets and informing clinical judgment in distinguishing MCI from dementia. To date no formal meta-analysis of extant studies has been conducted to compare the efficacy of such procedures. A meta-analysis was conducted to synthesize the sensitivity and specificity of AI and machine learning programs in distinguishing between MCI and dementia as compared to traditional diagnostic protocols. Data Selection A search of studies using EBSCOhost databases using the keywords: “artificial intelligence,” “machine learning,” “MCI,” and “dementia” retrieved a total of 127 studies. Excluded were 106 studies due to non-reporting of sensitivity and specificity data. In total, 21 studies were included in the present meta-analysis. Data Synthesis Sensitivity and specificity data as well as the number of true-false categorizations were extracted and analyzed using OpenMeta[Analyst]. A bivariate correlation produced a summary point with sensitivity of 82% and specificity of 82%. A follow-up Rutter-Gatsonis multivariate correlation HSROC curve was created to correct for significant correlations (47%), and produced an adjusted mean specificity of 79% and sensitivity of 83%. Conclusions Results suggest AI and machine-learning algorithms are effective in distinguishing MCI from dementia. AI procedures have potential in aiding clinical judgment given a larger body of empirical research.

Olfactory Phenotypes Differentiate Cognitively Unimpaired Seniors from Alzheimer’s Disease and Mild Cognitive Impairment: A Combined Machine Learning and Traditional Statistical Approach

2021 ◽  
pp. 1-10
Author(s):  
Jennifer Li ◽  
Andres M. Bur ◽  
Mark R. Villwock ◽  
Suraj Shankar ◽  
Gracie Palmer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Subjective Data ◽  
Disease States ◽  
Olfactory Testing

Background: Olfactory dysfunction (OD) is an early symptom of Alzheimer’s disease (AD). However, olfactory testing is not commonly performed to test OD in the setting of AD. Objective: This work investigates objective OD as a non-invasive biomarker for accurately classifying subjects as cognitively unimpaired (CU), mild cognitive impairment (MCI), and AD. Methods: Patients with MCI (n = 24) and AD (n = 24), and CU (n = 33) controls completed two objective tests of olfaction (Affordable, Rapid, Olfactory Measurement Array –AROMA; Sniffin’ Sticks Screening 12 Test –SST12). Demographic and subjective sinonasal and olfaction symptom information was also obtained. Analyses utilized traditional statistics and machine learning to determine olfactory variables, and combinations of variables, of importance for differentiating normal and disease states. Results: Inability to correctly identify a scent after detection was a hallmark of MCI/AD. AROMA was superior to SST12 for differentiating MCI from AD. Performance on the clove scent was significantly different between all three groups. AROMA regression modeling yielded six scents with AUC of the ROC of 0.890 (p <  0.001). Random forest model machine learning algorithms considering AROMA olfactory data successfully predicted MCI versus AD disease state. Considering only AROMA data, machine learning algorithms were 87.5%accurate (95%CI 0.4735, 0.9968). Sensitivity and specificity were 100%and 75%, respectively with ROC of 0.875. When considering AROMA and subject demographic and subjective data, the AUC of the ROC increased to 0.9375. Conclusion: OD differentiates CUs from those with MCI and AD and can accurately predict MCI versus AD. Leveraging OD data may meaningfully guide management and research decisions.

scholarly journals PERFORMANCE COMPARISON OF MACHINE LEARNING ALGORITHMS FOR PREDICTIVE MAINTENANCE

2020 ◽  
Vol 10 (3) ◽  
pp. 32-35
Author(s):  
Jakub Gęca
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Predictive Maintenance ◽  
Model Parameters ◽  
Data Set ◽  
Reduction Techniques ◽  
Machine Reliability ◽  
Dimensionality Reduction Techniques

The consequences of failures and unscheduled maintenance are the reasons why engineers have been trying to increase the reliability of industrial equipment for years. In modern solutions, predictive maintenance is a frequently used method. It allows to forecast failures and alert about their possibility. This paper presents a summary of the machine learning algorithms that can be used in predictive maintenance and comparison of their performance. The analysis was made on the basis of data set from Microsoft Azure AI Gallery. The paper presents a comprehensive approach to the issue including feature engineering, preprocessing, dimensionality reduction techniques, as well as tuning of model parameters in order to obtain the highest possible performance. The conducted research allowed to conclude that in the analysed case , the best algorithm achieved 99.92% accuracy out of over 122 thousand test data records. In conclusion, predictive maintenance based on machine learning represents the future of machine reliability in industry.

Validity of a novel computerized screening test system for mild cognitive impairment

2018 ◽  
Vol 30 (10) ◽  
pp. 1455-1463 ◽  
Author(s):  
Jin-Hyuck Park ◽  
Minye Jung ◽  
Jongbae Kim ◽  
Hae Yean Park ◽  
Jung-Ran Kim ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Elderly People ◽  
Predictive Value ◽  
Screening Test ◽  
Concurrent Validity ◽  
Test System ◽  
Screening Tests ◽  
Validity And Reliability ◽  
Healthy Elderly

ABSTRACTBackground:The mobile screening test system for screening mild cognitive impairment (mSTS-MCI) was developed for clinical use. However, the clinical usefulness of mSTS-MCI to detect elderly with MCI from those who are cognitively healthy has yet to be validated. Moreover, the comparability between this system and traditional screening tests for MCI has not been evaluated.Objective:The purpose of this study was to examine the validity and reliability of the mSTS-MCI and confirm the cut-off scores to detect MCI.Method:The data were collected from 107 healthy elderly people and 74 elderly people with MCI. Concurrent validity was examined using the Korean version of Montreal Cognitive Assessment (MoCA-K) as a gold standard test, and test–retest reliability was investigated using 30 of the study participants at four-week intervals. The sensitivity, specificity, positive predictive value, and negative predictive value (NPV) were confirmed through Receiver Operating Characteristic (ROC) analysis, and the cut-off scores for elderly people with MCI were identified.Results:Concurrent validity showed statistically significant correlations between the mSTS-MCI and MoCA-K and test–rests reliability indicated high correlation. As a result of screening predictability, the mSTS-MCI had a higher NPV than the MoCA-K.Conclusions:The mSTS-MCI was identified as a system with a high degree of validity and reliability. In addition, the mSTS-MCI showed high screening predictability, indicating it can be used in the clinical field as a screening test system for mild cognitive impairment.

scholarly journals Non-Invasive Risk Stratification of Hypertension: A Systematic Comparison of Machine Learning Algorithms

2020 ◽  
Vol 9 (3) ◽  
pp. 34
Author(s):  
Giovanna Sannino ◽  
Ivanoe De Falco ◽  
Giuseppe De Pietro
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Risk Stratification ◽  
Learning Algorithms ◽  
Circulatory System ◽  
Machine Learning Algorithms ◽  
Learning Mechanisms ◽  
Data Set ◽  
Non Invasive ◽  
Blood Pressure Estimation

One of the most important physiological parameters of the cardiovascular circulatory system is Blood Pressure. Several diseases are related to long-term abnormal blood pressure, i.e., hypertension; therefore, the early detection and assessment of this condition are crucial. The identification of hypertension, and, even more the evaluation of its risk stratification, by using wearable monitoring devices are now more realistic thanks to the advancements in Internet of Things, the improvements of digital sensors that are becoming more and more miniaturized, and the development of new signal processing and machine learning algorithms. In this scenario, a suitable biomedical signal is represented by the PhotoPlethysmoGraphy (PPG) signal. It can be acquired by using a simple, cheap, and wearable device, and can be used to evaluate several aspects of the cardiovascular system, e.g., the detection of abnormal heart rate, respiration rate, blood pressure, oxygen saturation, and so on. In this paper, we take into account the Cuff-Less Blood Pressure Estimation Data Set that contains, among others, PPG signals coming from a set of subjects, as well as the Blood Pressure values of the latter that is the hypertension level. Our aim is to investigate whether or not machine learning methods applied to these PPG signals can provide better results for the non-invasive classification and evaluation of subjects’ hypertension levels. To this aim, we have availed ourselves of a wide set of machine learning algorithms, based on different learning mechanisms, and have compared their results in terms of the effectiveness of the classification obtained.

scholarly journals Mild cognitive impairment, dementia, and cognitive dysfunction screening using machine learning

2020 ◽  
Vol 48 (7) ◽  
pp. 030006052093688
Author(s):  
Daehyuk Yim ◽  
Tae Young Yeo ◽  
Moon Ho Park
Keyword(s):  
Machine Learning ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cognitive Dysfunction ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Kappa Statistics ◽  
Machine Learning Algorithm ◽  
Neuropsychological Screening ◽  
Dementia Screening

Objective To develop a machine learning algorithm to identify cognitive dysfunction based on neuropsychological screening test results. Methods This retrospective study included 955 participants: 341 participants with dementia (dementia), 333 participants with mild cognitive impairment (MCI), and 341 participants who were cognitively healthy. All participants underwent evaluations including the Mini-Mental State Examination and the Montreal Cognitive Assessment. Each participant’s caregiver or informant was surveyed using the Korean Dementia Screening Questionnaire at the same visit. Different machine learning algorithms were applied, and their overall accuracies, Cohen’s kappa, receiver operating characteristic curves, and areas under the curve (AUCs) were calculated. Results The overall screening accuracies for MCI, dementia, and cognitive dysfunction (MCI or dementia) using a machine learning algorithm were approximately 67.8% to 93.5%, 96.8% to 99.9%, and 75.8% to 99.9%, respectively. Their kappa statistics ranged from 0.351 to 1.000. The AUCs of the machine learning models were statistically superior to those of the competing screening model. Conclusion This study suggests that a machine learning algorithm can be used as a supportive tool in the screening of MCI, dementia, and cognitive dysfunction.

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