Evidence map›Paper›PMID 34966518›Full record

ArticleJournal of healthcare engineering2021

Detecting Cognitive Impairment Status Using Keystroke Patterns and Physical Activity Data among the Older Adults: A Machine Learning Approach.

Mohammad Nahid Hossain, Mohammad Helal Uddin, K Thapa, Md Abdullah Al Zubaer, Md Shafiqul Islam, Jiyun Lee, JongSu Park, S-H Yang

Abstract read
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In one paragraph

Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Mohammad Nahid HossainDepartment of Electronic Engineering, Kwangwoon University, Seoul 139-701, Republic of Korea.ORCID 0000-0002-7423-2096
Mohammad Helal UddinDepartment of Electronic Engineering, Kwangwoon University, Seoul 139-701, Republic of Korea.
K ThapaDepartment of Electronic Engineering, Kwangwoon University, Seoul 139-701, Republic of Korea.
Md Abdullah Al ZubaerDepartment of Electronic Engineering, Kwangwoon University, Seoul 139-701, Republic of Korea.
Md Shafiqul IslamDepartment of Electronic Engineering, Kwangwoon University, Seoul 139-701, Republic of Korea.
Jiyun LeeDepartment of Electronic Engineering, Kwangwoon University, Seoul 139-701, Republic of Korea.ORCID 0000-0001-7470-5239
JongSu ParkDepartment of Electronic Engineering, Kwangwoon University, Seoul 139-701, Republic of Korea.ORCID 0000-0002-1991-1483
S-H YangSmart H&B Technology Laboratory, Department of Electronic Engineering, Kwangwoon University, Seoul 139-701, Republic of Korea.ORCID 0000-0001-8856-764X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cognitive impairment has a significantly negative impact on global healthcare and the community. Holding a person's cognition and mental retention among older adults is improbable with aging. Early detection of cognitive impairment will decline the most significant impact of extended disease to permanent mental damage. This paper aims to develop a machine learning model to detect and differentiate cognitive impairment categories like severe, moderate, mild, and normal by analyzing neurophysical and physical data. Keystroke and smartwatch have been used to extract individuals' neurophysical and physical data, respectively. An advanced ensemble learning algorithm named Gradient Boosting Machine (GBM) is proposed to classify the cognitive severity level (absence, mild, moderate, and severe) based on the Standardised Mini-Mental State Examination (SMMSE) questionnaire scores. The statistical method "Pearson's correlation" and the wrapper feature selection technique have been used to analyze and select the best features. Then, we have conducted our proposed algorithm GBM on those features. And the result has shown an accuracy of more than 94%. This paper has added a new dimension to the state-of-the-art to predict cognitive impairment by implementing neurophysical data and physical data together.

Indexed as

Cognitive DysfunctionAgedCognitionExerciseHumansMachine LearningMental Status and Dementia Tests

Identifiers

PMID34966518
PMCPMC8712156

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.