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Machine Learning in Hypertension Detection: A Study on World Hypertension Day Data.
- Source :
- Journal of Medical Systems; 2023, Vol. 47 Issue 1, p1-10, 10p, 1 Diagram, 4 Charts, 2 Graphs
- Publication Year :
- 2023
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- HYPERTENSION risk factors
HYPERTENSION
DECISION trees
SUPPORT vector machines
PREDICTIVE tests
RESEARCH evaluation
MEDICAL screening
MACHINE learning
RANDOM forest algorithms
ACCURACY
MEDICAL protocols
QUESTIONNAIRES
DESCRIPTIVE statistics
RESEARCH funding
SENSITIVITY & specificity (Statistics)
RECEIVER operating characteristic curves
LOGISTIC regression analysis
ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 01485598
- Volume :
- 47
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Journal of Medical Systems
- Publication Type :
- Academic Journal
- Accession number :
- 161820946
- Full Text :
- https://doi.org/10.1007/s10916-022-01900-5