Intelligent Virtual Ambulance Model using Predictive Learning
Most people are unable to identify their actual illness, and many patients die before they are
hospitalized. Therefore, in this research, a health monitoring system (HMS) is proposed and …
hospitalized. Therefore, in this research, a health monitoring system (HMS) is proposed and …
Heart disease prediction using distinct artificial intelligence techniques: performance analysis and comparison
Consolidated efforts have been made to enhance the treatment and diagnosis of heart
disease due to its detrimental effects on society. As technology and medical diagnostics …
disease due to its detrimental effects on society. As technology and medical diagnostics …
An effective approach for early liver disease prediction and sensitivity analysis
The liver is one of the most vital organs of the human body. Even when partially injured, it
functions normally. Therefore, detecting liver diseases at the early stages is challenging …
functions normally. Therefore, detecting liver diseases at the early stages is challenging …
[PDF][PDF] A survey on various machine learning approaches for ECG analysis
Electrocardiogram (ECG) is a P, QRS and T wave demonstrating the electrical activity of the
heart. Feature extraction and segmentation in ECG plays a significant role in diagnosing …
heart. Feature extraction and segmentation in ECG plays a significant role in diagnosing …
Machine learning-based screening solution for COVID-19 cases investigation: socio-demographic and behavioral factors analysis and COVID-19 Detection
KMA Uddin, FS Prity, M Tasnim, SN Jannat… - Human-Centric …, 2023 - Springer
The COVID-19 pandemic has unleashed an unprecedented global crisis, releasing a wave
of illness, mortality, and economic disarray of unparalleled proportions. Numerous societal …
of illness, mortality, and economic disarray of unparalleled proportions. Numerous societal …
Superiority of classification tree versus cluster, fuzzy and discriminant models in a heartbeat classification system
V Krasteva, I Jekova, R Leber, R Schmid, R Abächerli - PloS one, 2015 - journals.plos.org
This study presents a 2-stage heartbeat classifier of supraventricular (SVB) and ventricular
(VB) beats. Stage 1 makes computationally-efficient classification of SVB-beats, using …
(VB) beats. Stage 1 makes computationally-efficient classification of SVB-beats, using …
Personalized federated learning for ECG classification based on feature alignment
R Tang, J Luo, J Qian, J Jin - Security and Communication …, 2021 - Wiley Online Library
Electrocardiogram (ECG) data classification is a hot research area for its application in
medical information processing. However, insufficient data, privacy preserve, and local …
medical information processing. However, insufficient data, privacy preserve, and local …
A micro neural network for healthcare sensor data stream classification in sustainable and smart cities
A smart city is an intelligent space, in which large amounts of data are collected and
analyzed using low‐cost sensors and automatic algorithms. The application of artificial …
analyzed using low‐cost sensors and automatic algorithms. The application of artificial …
A deep multi-task learning approach for ECG data analysis
Deep learning is an advanced representation learning method and can automatically
discover hidden features from raw data. Researchers have attempted to adopt it for ECG …
discover hidden features from raw data. Researchers have attempted to adopt it for ECG …
A deep multi-task learning approach for bioelectrical signal analysis
Deep learning is a promising technique for bioelectrical signal analysis, as it can
automatically discover hidden features from raw data without substantial domain knowledge …
automatically discover hidden features from raw data without substantial domain knowledge …