Abstract
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.
Highlights
IntroductionMachine learning methods have been used to determine the hypertension types based on personal data
A machine learning approach for the classification of hypertension types based on the personal features comprising sex, age, height, weight, systolic blood pressure, diastolic blood pressure, heart rate, and BMI has been proposed. ere are four types of hypertension as follows: normal, prehypertension, stage-1 hypertension, and stage-2 hypertension
High blood pressure does not cause any symptoms in many people because they are unaware of the presence of high blood pressure which can damage the heart, the kidneys, and even the brain. erefore, the diagnosis of hypertension disease is so significant with respect to human health
Summary
Machine learning methods have been used to determine the hypertension types based on personal data. As the machine learning algorithm, four different classification algorithms have been used to classify the types of hypertension in this paper. Ere are two types of blood pressure: systolic blood pressure (SBP) and diastolic blood pressure (DBP). SBP shows the pressure in the blood vessels when the heart beats. DBP represents the pressure in the vessels between beats. Hypertension is diagnosed when the SBP value is equal to or greater than 140 mmHg for both days and the DBP value is greater than or equal to 90 mmHg for both days when measuring on two different days.
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