Gabriel F S Silva1, Thales P Fagundes2, Bruno C Teixeira2, Alexandre D P Chiavegatto Filho3. 1. Department of Epidemiology, School of Public Health, University of São Paulo, São Paulo, SP, Brazil. 2. Laboratory of Big Data and Predictive Analysis in Healthcare, School of Public Health, University of São Paulo, São Paulo, SP, Brazil. 3. Department of Epidemiology, School of Public Health, University of São Paulo, São Paulo, SP, Brazil. alexdiasporto@usp.br.
Abstract
PURPOSE OF REVIEW: To provide an overview of the literature regarding the use of machine learning algorithms to predict hypertension. A systematic review was performed to select recent articles on the subject. RECENT FINDINGS: The screening of the articles was conducted using a machine learning algorithm (ASReview). A total of 21 articles published between January 2018 and May 2021 were identified and compared according to variable selection, train-test split, data balancing, outcome definition, final algorithm, and performance metrics. Overall, the articles achieved an area under the ROC curve (AUROC) between 0.766 and 1.00. The algorithms most frequently identified as having the best performance were support vector machines (SVM), extreme gradient boosting (XGBoost), and random forest. Machine learning algorithms are a promising tool to improve preventive clinical decisions and targeted public health policies for hypertension. However, technical factors such as outcome definition, availability of the final code, predictive performance, explainability, and data leakage need to be consistently and critically evaluated.
PURPOSE OF REVIEW: To provide an overview of the literature regarding the use of machine learning algorithms to predict hypertension. A systematic review was performed to select recent articles on the subject. RECENT FINDINGS: The screening of the articles was conducted using a machine learning algorithm (ASReview). A total of 21 articles published between January 2018 and May 2021 were identified and compared according to variable selection, train-test split, data balancing, outcome definition, final algorithm, and performance metrics. Overall, the articles achieved an area under the ROC curve (AUROC) between 0.766 and 1.00. The algorithms most frequently identified as having the best performance were support vector machines (SVM), extreme gradient boosting (XGBoost), and random forest. Machine learning algorithms are a promising tool to improve preventive clinical decisions and targeted public health policies for hypertension. However, technical factors such as outcome definition, availability of the final code, predictive performance, explainability, and data leakage need to be consistently and critically evaluated.
Authors: Desmond Chuang Kiat Soh; E Y K Ng; V Jahmunah; Shu Lih Oh; Ru San Tan; U Rajendra Acharya Journal: Comput Biol Med Date: 2020-09-17 Impact factor: 4.589