Machine Learning in Hypertension Detection: A Study on World Hypertension Day Data.
Many modifiable and non-modifiable risk factors have been associated with hypertension. However, current screening programs are still failing in identifying individuals at higher risk of hypertension. Given the major impact of high blood pressure on cardiovascular events and mortality, there is an u...
| Publicado en: | Journal of Medical Systems Vol. 47; no. 1; pp. 1 - 11 |
|---|---|
| Autores principales: | , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
2023
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=161820946&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 161820946 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 2023 vid: 47 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 161820946 161820946 161820946 10.1007/s10916-022-01900-5 161820946 ppf: 1 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Machine Learning in Hypertension Detection: A Study on World Hypertension Day Data. aug: au: Montagna, Sara Pengo, Martino Francesco Ferretti, Stefano Borghi, Claudio Ferri, Claudio Grassi, Guido Muiesan, Maria Lorenza Parati, Gianfranco affil: DiSPeA–University of Urbino Carlo Bo, Piazza della Repubblica 13, 61029, Urbino, Italy sug: subj: Hypertension Diagnosis Hypertension Risk Factors Health Screening Methods Machine Learning Algorithms Evaluation Predictive Value of Tests Evaluation Human Male Female Adult Middle Age Aged Questionnaires Decision Trees Random Forest Protocols Support Vector Machine Sensitivity and Specificity Descriptive Statistics Validity Precision ROC Curve Logistic Regression Funding Source Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Many modifiable and non-modifiable risk factors have been associated with hypertension. However, current screening programs are still failing in identifying individuals at higher risk of hypertension. Given the major impact of high blood pressure on cardiovascular events and mortality, there is an urgent need to find new strategies to improve hypertension detection. We aimed to explore whether a machine learning (ML) algorithm can help identifying individuals predictors of hypertension. We analysed the data set generated by the questionnaires administered during the World Hypertension Day from 2015 to 2019. A total of 20206 individuals have been included for analysis. We tested five ML algorithms, exploiting different balancing techniques. Moreover, we computed the performance of the medical protocol currently adopted in the screening programs. Results show that a gain of sensitivity reflects in a loss of specificity, bringing to a scenario where there is not an algorithm and a configuration which properly outperforms against the others. However, Random Forest provides interesting performances (0.818 sensitivity – 0.629 specificity) compared with medical protocols (0.906 sensitivity – 0.230 specificity). Detection of hypertension at a population level still remains challenging and a machine learning approach could help in making screening programs more precise and cost effective, when based on accurate data collection. More studies are needed to identify new features to be acquired and to further improve the performances of ML models. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|