Evidence map›Paper›PMID 32565878›Full record

ArticleComputational and mathematical methods in medicine2020

Influences of Daily Life Habits on Risk Factors of Stroke Based on Decision Tree and Correlation Matrix.

Zeguo Shao, Yuhong Xiang, Yingchao Zhu, Aiqin Fan, Peng Zhang

Open access · hybridAbstract read
In one paragraph

Article in Computational and mathematical methods in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 35% of its field
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 9 citations in OpenAlex.

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4 · The record

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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

5 authors at 5 institutions in 1 country.

Zeguo ShaoSchool of Medical Instrumentation, Shanghai University of Medicine &Health Sciences, Shanghai 201318, China.
Yuhong XiangSchool of Medical Instrumentation, Shanghai University of Medicine &Health Sciences, Shanghai 201318, China.
Yingchao ZhuNursing Department, Shanghai Pudong New District Zhoupu Hospital, Shanghai 201318, China.
Aiqin FanPudong New Area Lingqiao Community Health Service Center, Shanghai 200137, China.
Peng ZhangSchool of Clinical Medicine, Shanghai University of Medicine & Health Sciences, Shanghai 201318, China.ORCID https://orcid.org/0000-0002-9997-2576
Fudan University · CNPudong Medical Center · CNPudong New Area People's Hospital · CNShanghai Medical Information Center · CNShanghai University of Medicine and Health Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo explore the influences of smoking, alcohol consumption, drinking tea, diet, sleep, and exercise on the risk of stroke and relationships among the factors, present corresponding knowledge-based rules, and provide a scientific basis for assessment and intervention of risk factors of stroke.

methodsThe decision tree C4.5 algorithm was optimized and utilized to establish a model for stroke risk assessment; then, the main risk factors of stroke (including hypertension, dyslipidemia, diabetes, atrial fibrillation, body mass index (BMI), history of stroke, family history of stroke, and transient ischemic attack (TIA)) and daily habits (e.g., smoking, alcohol consumption, drinking tea, diet, sleep, and exercise) were analyzed; corresponding knowledge-based rules were finally presented. Establish a correlation matrix of stroke risk factors and analyze the relationship between stroke risk factors.

resultsThe accuracy of the established model for stroke risk assessment was 87.53%, and the kappa coefficient was 0.8344, which was superior to that of the random forest and Logistic algorithm. Additionally, 37 knowledge-based rules that can be used for prevention of risk factors of stroke were derived and verified. According to in-depth analysis of risk factors of stroke, the values of smoking, exercise, sleep, drinking tea, alcohol consumption, and diet were 6.00, 7.00, 8.67, 9.33, 10.00, 10.60, and 10.75, respectively, indicating that their influence on risk factors of stroke was reduced in turn; on the one hand, smoking and exercise were strongly associated with other risk factors of stroke; on the other hand, sleep, drinking tea, alcohol consumption, and diet were not firmly associated with other risk factors of stroke, and they were relatively tightly associated with smoking and exercise.

conclusionsEstablishment of a model for stroke risk assessment, analysis of factors influencing risk factors of stroke, analysis of relationships among those factors, and derivation of knowledge-based rules are helpful for prevention and treatment of stroke.

Indexed as

HabitsAdolescentAdultAgedAged, 80 and overAlcohol DrinkingAlgorithmsChinaComputational BiologyDecision TreesDietExerciseFemaleHumansKnowledge BasesLife StyleTea

Identifiers

PMID32565878
PMCPMC7285386
OpenAlexW3033846845

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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.