Survey and evaluation of hypertension machine learning research

C Du Toit, TQB Tran, N Deo, S Aryal, S Lip… - Journal of the …, 2023 - Am Heart Assoc
Background Machine learning (ML) is pervasive in all fields of research, from automating
tasks to complex decision‐making. However, applications in different specialities are …

[HTML][HTML] Modular Clinical Decision Support Networks (MoDN)—Updatable, interpretable, and portable predictions for evolving clinical environments

C Trottet, T Vogels, K Keitel, AV Kulinkina… - PLOS digital …, 2023 - journals.plos.org
Clinical Decision Support Systems (CDSS) have the potential to improve and standardise
care with probabilistic guidance. However, many CDSS deploy static, generic rule-based …

Predicting hypertension based on machine learning methods: A case study in northwest vietnam

TT Oanh, NT Tung - Mobile Networks and Applications, 2022 - Springer
Hypertension is a major risk factor for cardiovascular diseases (CVD). Identifying the
persons at these high risks plays an important role because it would save time and money …

Machine learning techniques based on security management in smart cities using robots

M Zhang, X Wang, VE Sathishkumar, V Sivakumar - Work, 2021 - content.iospress.com
BACKGROUND: Nowadays, the growth of smart cities is enhanced gradually, which collects
a lot of information and communication technologies that are used to maximize the quality of …

Machine learning and blood pressure

P Santhanam, RS Ahima - The Journal of Clinical Hypertension, 2019 - Wiley Online Library
Abstract Machine learning (ML) is a type of artificial intelligence (AI) based on pattern
recognition. There are different forms of supervised and unsupervised learning algorithms …

PPG signals for hypertension diagnosis: A novel method using deep learning models

G Frederick - arXiv preprint arXiv:2304.06952, 2023 - arxiv.org
Hypertension is a medical condition characterized by high blood pressure, and classifying it
into its various stages is crucial to managing the disease. In this project, a novel method is …

EMG signal processing for hand motion pattern recognition using machine learning algorithms

Y Zhou, C Chen, J Ni, G Ni, M Li, G Xu… - Archives of …, 2020 - scientificarchives.com
Electromyography (EMG) signal processing for assistive medical device control has been
developed for clinical rehabilitation. The accuracy of operation and responsive time are still …

Chronic diseases monitoring and diagnosis system based on features selection and machine learning predictive models

SA El-Rahman, A Saleh Alluhaidan, RA AlRashed… - Soft Computing, 2022 - Springer
This paper promotes better life quality and lifestyle for patients. We attain this goal by
creating a mobile application that analyses patient's medical records, such as diabetes …

Particle swarm optimization of modular neural networks for obtaining the trend of blood pressure

I Miramontes, P Melin, G Prado-Arechiga - Intuitionistic and type-2 fuzzy …, 2020 - Springer
In this work, the optimization of a modular neural network for obtaining the trend of blood
pressure is presented. Three modules are used, the first for obtaining the systolic pressure …

Studi Komparatif Model Klasifikasi Kerentanan Penyakit Jantung Menggunakan Algoritma Machine Learning

W Lestari, S Sumarlinda - SATIN-Sains dan Teknologi Informasi, 2023 - sia.lab.sar.ac.id
Penyakit jantung merupakan salah satu penyebab kematian baik di dunia maupun
Indonesia. Perhatian awal dari penyakit jantung akan memudahkan pencegahan dan …