Classification of diabetic retinopathy with feature selection over deep features using nature-inspired wrapper methods
M Canayaz - Applied Soft Computing, 2022 - Elsevier
Diabetic retinopathy (DR) is the most common cause of blindness in middle-aged people. It
shows that an automatic image evaluation system is needed in the diagnosis of this disease …
shows that an automatic image evaluation system is needed in the diagnosis of this disease …
Survey of deep learning techniques for disease prediction based on omics data
X Yu, S Zhou, H Zou, Q Wang, C Liu, M Zang, T Liu - Human Gene, 2023 - Elsevier
In the era of big data, computer science has been applied to every aspect of biomedical
field. At the same time, transforming biomedical data into valuable knowledge is one of the …
field. At the same time, transforming biomedical data into valuable knowledge is one of the …
The potential and challenges of Health 4.0 to face COVID-19 pandemic: A rapid review
CI Loeza-Mejía, E Sánchez-DelaCruz… - Health and …, 2021 - Springer
The COVID-19 pandemic has generated the need to evolve health services to reduce the
risk of contagion and promote a collaborative environment even remotely. Advances in …
risk of contagion and promote a collaborative environment even remotely. Advances in …
Comparative study of optimum medical diagnosis of human heart disease using machine learning technique with and without sequential feature selection
GN Ahmad, S Ullah, A Algethami, H Fatima… - ieee …, 2022 - ieeexplore.ieee.org
Predicting heart disease is regarded as one of the most difficult challenges in the health-
care profession. To predict cardiac disease, researchers employed a variety of algorithms …
care profession. To predict cardiac disease, researchers employed a variety of algorithms …
Effective heart disease prediction using novel MLP-EBMDA approach
D Deepika, N Balaji - Biomedical Signal Processing and Control, 2022 - Elsevier
Heart disease prediction is more important to prevent the death rate. The death rate
increases due to lack of initial detection of heart disease in humans. To predict heart disease …
increases due to lack of initial detection of heart disease in humans. To predict heart disease …
[HTML][HTML] A deep learning segmentation-classification pipeline for x-ray-based covid-19 diagnosis
R Hertel, R Benlamri - Biomedical Engineering Advances, 2022 - Elsevier
Over the past year, the AI community has constructed several deep learning models for
diagnosing COVID-19 based on the visual features of chest X-rays. While deep learning …
diagnosing COVID-19 based on the visual features of chest X-rays. While deep learning …
An efficient and privacy-preserving scheme for disease prediction in modern healthcare systems
S Padinjappurathu Gopalan, CL Chowdhary, C Iwendi… - Sensors, 2022 - mdpi.com
With the Internet of Things (IoT), mobile healthcare applications can now offer a variety of
dimensionalities and online services. Disease Prediction Systems (DPS) increase the speed …
dimensionalities and online services. Disease Prediction Systems (DPS) increase the speed …
Novel framework based on ensemble classification and secure feature extraction for COVID-19 critical health prediction
The Covid outbreak necessitated the use of an automated method for treating patients with
critical symptoms. Increasing use of the Internet of Things (IoT) and smart devices requires …
critical symptoms. Increasing use of the Internet of Things (IoT) and smart devices requires …
Classification of heart disease using adaptive Harris hawk optimization-based clustering algorithm and enhanced deep genetic algorithm
R Balamurugan, S Ratheesh, YM Venila - Soft computing, 2022 - Springer
Heart disease is the most life-threatening disease globally, affecting human life very
critically. On-time and precise diagnosis of heart disease is vital for the prevention and …
critically. On-time and precise diagnosis of heart disease is vital for the prevention and …
Energy-efficient online continual learning for time series classification in nanorobot-based smart health
Nanorobots have been used in smart health to collect time series data such as
electrocardiograms and electroencephalograms. Real-time classification of dynamic time …
electrocardiograms and electroencephalograms. Real-time classification of dynamic time …