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Open AccessArticle

A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data

School of Reliability and Systems Engineering, Beihang University, Beijing 100191 China
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Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Diagnostics 2019, 9(4), 178; https://doi.org/10.3390/diagnostics9040178
Received: 26 September 2019 / Revised: 4 November 2019 / Accepted: 5 November 2019 / Published: 7 November 2019
(This article belongs to the Special Issue Artificial Intelligence in Diagnostics)
The outcomes of hypertension refer to the death or serious complications (such as myocardial infarction or stroke) that may occur in patients with hypertension. The outcomes of hypertension are very concerning for patients and doctors, and are ideally avoided. However, there is no satisfactory method for predicting the outcomes of hypertension. Therefore, this paper proposes a prediction method for outcomes based on physical examination indicators of hypertension patients. In this work, we divide the patients’ outcome prediction into two steps. The first step is to extract the key features from the patients’ many physical examination indicators. The second step is to use the key features extracted from the first step to predict the patients’ outcomes. To this end, we propose a model combining recursive feature elimination with a cross-validation method and classification algorithm. In the first step, we use the recursive feature elimination algorithm to rank the importance of all features, and then extract the optimal features subset using cross-validation. In the second step, we use four classification algorithms (support vector machine (SVM), C4.5 decision tree, random forest (RF), and extreme gradient boosting (XGBoost)) to accurately predict patient outcomes by using their optimal features subset. The selected model prediction performance evaluation metrics are accuracy, F1 measure, and area under receiver operating characteristic curve. The 10-fold cross-validation shows that C4.5, RF, and XGBoost can achieve very good prediction results with a small number of features, and the classifier after recursive feature elimination with cross-validation feature selection has better prediction performance. Among the four classifiers, XGBoost has the best prediction performance, and its accuracy, F1, and area under receiver operating characteristic curve (AUC) values are 94.36%, 0.875, and 0.927, respectively, using the optimal features subset. This article’s prediction of hypertension outcomes contributes to the in-depth study of hypertension complications and has strong practical significance. View Full-Text
Keywords: hypertension outcomes; feature selection; recursive feature elimination; classification algorithm; XGBoost; prediction hypertension outcomes; feature selection; recursive feature elimination; classification algorithm; XGBoost; prediction
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Chang, W.; Liu, Y.; Xiao, Y.; Yuan, X.; Xu, X.; Zhang, S.; Zhou, S. A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data. Diagnostics 2019, 9, 178.

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