Key result
Machine learning algorithms, specifically C4.5 decision tree and random forest, achieved 99.5% accuracy in automatically classifying hypertension types based on personal features.
Why the study?
Hypertension is a prevalent condition requiring early detection for timely treatment, but classifying hypertension types based on personal data using machine learning algorithms had not been carried out.
Can machine learning algorithms accurately classify hypertension types based on personal features?
Can machine learning algorithms accurately classify hypertension types based on personal features?
Machine learning algorithms, particularly C4.5 decision tree and random forest, can classify hypertension types with very high accuracy based on basic personal features.
May enable automated hypertension subtyping; leaves open clinical utility pending prospective validation.
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/m 2 ) 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.
No takes yet. Share an insight, caveat, or question.
Nour et al. (2020) studied Hypertension. Machine learning algorithms (C4.5 DTC, random forest, LDA, LSVM) was evaluated on Classification accuracy of hypertension types. Machine learning algorithms, specifically C4.5 decision tree and random forest, achieved 99.5% accuracy in automatically classifying hypertension types based on personal features.