An automatic premature ventricular contraction recognition system based on imbalanced dataset and pre-trained residual network using transfer learning on ECG …

H Ullah, MBB Heyat, F Akhtar, AY Muaad… - Diagnostics, 2022 - mdpi.com
The development of automatic monitoring and diagnosis systems for cardiac patients over
the internet has been facilitated by recent advancements in wearable sensor devices from …

IoMT meets machine learning: From edge to cloud chronic diseases diagnosis system

N Nigar, A Jaleel, S Islam… - Journal of Healthcare …, 2023 - Wiley Online Library
In conventional healthcare, real‐time monitoring of patient records and information mining
for timely diagnosis of chronic diseases under certain health conditions is a crucial process …

A novel attention-based cross-modal transfer learning framework for predicting cardiovascular disease

NK Karthikeyan - Computers in Biology and Medicine, 2024 - Elsevier
Cardiovascular disease (CVD) remains a leading cause of death globally, presenting
significant challenges in early detection and treatment. The complexity of CVD arises from its …

HybDeepNet: a hybrid deep learning model for detecting cardiac arrhythmia from ECG signals

RS Ram, J Akilandeswari, MV Kumar - Information Technology and Control, 2023 - itc.ktu.lt
The problem to be addressed is the high mortality rate of heart disease and the need for
reliable and early detection techniques to prevent fatalities. Several clinical tests, including …

A deep ensemble network model for classifying and predicting breast cancer

AAV Subramanian, JP Venugopal - Computational Intelligence, 2023 - Wiley Online Library
Breast cancer is one of the leading causes of death among women worldwide. In most
cases, the misinterpretation of medical diagnosis plays a vital role in increased fatality rates …

Classical, evolutionary, and deep learning approaches of automated heart disease prediction: a case study

CL Cocianu, CR Uscatu, K Kofidis, S Muraru… - Electronics, 2023 - mdpi.com
Cardiovascular diseases (CVDs) are the leading cause of death globally. Detecting this kind
of disease represents the principal concern of many scientists, and techniques belonging to …

Heart disease prognosis using D-GRU with logistic chaos honey badger optimization in IoMT framework

S Karthikeyini, G Vidhya, T Vetriselvi, K Deepa - Information Technology and …, 2023 - itc.ktu.lt
In recent years, heart disease has superseded several other contributory death factors. It is
challenging to predict an individual's risk of acquiring heart disease since it requires both …

Deep learning based cardiovascular disease risk factor prediction among type 2 diabetes mellitus patients

C Selvarathi, S Varadhaganapathy - Information Technology and Control, 2023 - itc.ktu.lt
Abstract Type 2 Diabetes Mellitus (T2DM) is a common chronic disease that is caused due
to insulin discharge disorder. Due to the complication of T2DM, the outcomes of this disease …

A temporal transformer-based fusion framework for morphological arrhythmia classification

N Anjum, KA Sathi, MA Hossain, MAA Dewan - Computers, 2023 - mdpi.com
By using computer-aided arrhythmia diagnosis tools, electrocardiogram (ECG) signal plays
a vital role in lowering the fatality rate associated with cardiovascular diseases (CVDs) and …

Arrhythmia Classification Algorithm Based on a Two‐Dimensional Image and Modified EfficientNet

C Zhao, W Yao, M Yi, C Wan… - Computational …, 2022 - Wiley Online Library
The classification and identification of arrhythmias using electrocardiogram (ECG) signals
are of great practical significance in the early prevention and diagnosis of cardiovascular …