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Automatic Classification of Hypertension Types Based on Personal Features by Machine Learning Algorithms
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-01-20
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Hypertension (high blood pressure) is an important disease seen among the public, and early detection of hypertension is significant for early treatment.
Hypertension is depicted as systolic blood pressure higher than 140 mmHg or diastolic blood pressure higher than 90 mmHg.
In this paper, in order to detect the hypertension types based on the personal information and features, four machine learning (ML) methods including C4.5 decision tree classifier (DTC), random forest, linear discriminant analysis (LDA), and linear support vector machine (LSVM) have been used and then compared with each other.
In the literature, we have first carried out the classification of hypertension types using classification algorithms based on personal data.
To further explain the variability of the classifier type, four different classifier algorithms were selected for solving this problem.
In the hypertension dataset, there are eight features including sex, age, height (cm), weight (kg), systolic blood pressure (mmHg), diastolic blood pressure (mmHg), heart rate (bpm), and BMI (kg/m2) to explain the hypertension status and then there are four classes comprising the normal (healthy), prehypertension, stage-1 hypertension, and stage-2 hypertension.
In the classification of the hypertension dataset, the obtained classification accuracies are 99.5%, 99.5%, 96.3%, and 92.7% using the C4.5 decision tree classifier, random forest, LDA, and LSVM.
The obtained results have shown that ML methods could be confidently used in the automatic determination of the hypertension types.
American Psychological Association (APA)
Nour, Majid& Polat, Kemal. 2020. Automatic Classification of Hypertension Types Based on Personal Features by Machine Learning Algorithms. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1194013
Modern Language Association (MLA)
Nour, Majid& Polat, Kemal. Automatic Classification of Hypertension Types Based on Personal Features by Machine Learning Algorithms. Mathematical Problems in Engineering No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1194013
American Medical Association (AMA)
Nour, Majid& Polat, Kemal. Automatic Classification of Hypertension Types Based on Personal Features by Machine Learning Algorithms. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1194013
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1194013