Hypertension prediction in adolescents using anthropometric measurements: Do machine learning models perform equally well?

• Main Authors: Chai, Soo See, Goh, Kok Luong, Cheah, Whye Lian, Chang, Yee Hui Robin, Ng, Giap Weng
• Format: Article
• Language: English; English
• Published: MDPI AG, Basel, Switzerland 2022
• Subjects: QA76.75-76.765 Computer software; RC31-1245 Internal medicine
• Online Access: https://eprints.ums.edu.my/id/eprint/32758/1/Hypertension%20prediction%20in%20adolescents%20using%20anthropometric%20measurements%2C%20Do%20machine%20learning%20models%20perform%20equally%20well.ABSTRACT.pdf; https://eprints.ums.edu.my/id/eprint/32758/2/Hypertension%20Prediction%20in%20Adolescents%20Using%20Anthropometric%20Measurements%2C%20Do%20Machine%20Learning%20Models%20Perform%20Equally%20Well.pdf; https://eprints.ums.edu.my/id/eprint/32758/; https://www.mdpi.com/2076-3417/12/3/1600; https://doi.org/10.3390/app12031600

The use of anthropometric measurements in machine learning algorithms for hypertension prediction enables the development of simple, non-invasive prediction models. However, different machine learning algorithms were utilized in conjunction with various anthropometric data, either alone or in combin...