Machine learning techniques for classification of diabetes and cardiovascular diseases

B Alić, L Gurbeta, A Badnjević - 2017 6th mediterranean …, 2017 - ieeexplore.ieee.org
This paper presents the overview of machine learning techniques in classification of
diabetes and cardiovascular diseases (CVD) using Artificial Neural Networks (ANNs) and …

Wavelet based deep learning approach for epilepsy detection

R Akut - Health information science and systems, 2019 - Springer
Electroencephalogram (EEG) signal contains vital details regarding electrical actions
performed by the brain. Analysis of these signals is important for epilepsy detection …

Efficient multi-level lung cancer prediction model using support vector machine classifier

BR Manju, V Athira, A Rajendran - IOP Conference Series …, 2021 - iopscience.iop.org
This paper aims at the requirement for an interactive learning framework which empowers
the successful checking of disorder in a patient. Principal component analysis stands out as …

Classification of prediabetes and type 2 diabetes using artificial neural network

D Sejdinović, L Gurbeta, A Badnjević… - … 2017: Proceedings of …, 2017 - Springer
In this paper development of Artificial Neural Network for classification of prediabetes and
type 2 diabetes (T2D) is presented. For development of this system 310 samples consisting …

Classification of metabolic syndrome patients using implemented expert system

B Alić, L Gurbeta, A Badnjević… - … 2017: Proceedings of …, 2017 - Springer
This paper presents the development of an Expert System for the classification of metabolic
syndrome (MetS). Two-layer feedforward Artificial Neural Network (ANN) with sigmoid …

Brainwaves stress pattern based on perceived stress scale test

NHA Hamid, N Sulaiman, ZH Murat… - 2015 IEEE 6th control …, 2015 - ieeexplore.ieee.org
This paper presents human stress pattern for alpha and beta waves obtained from EEG
Power Spectrum analysis. The EEG stress evaluation conducted between human Cohen's …

A novel convolution bi-directional gated recurrent unit neural network for emotion recognition in multichannel electroencephalogram signals

A Abgeena, S Garg - Technology and Health Care, 2023 - content.iospress.com
BACKGROUND: Recognising emotions in humans is a great challenge in the present era
and has several applications under affective computing. Deep learning (DL) is found as a …

Brain-region specific autism prediction from electroencephalogram signals using graph convolution neural network

NP Tigga, S Garg, N Goyal, J Raj… - Technology and Health …, 2024 - content.iospress.com
BACKGROUND: Brain variations are responsible for developmental impairments, including
autism spectrum disorder (ASD). EEG signals efficiently detect neurological conditions by …

[HTML][HTML] Wavelet transform as a helping tool during EEG analysis in children with Epilepsy

S Zahirovic, S Avdakovic… - Acta Informatica …, 2021 - ncbi.nlm.nih.gov
Background: Epilepsy is a brain disorder characterised by unpredictable and excessive
nerve cell activity that causes epileptic seizures. Epileptic seizures are more common in …

Opportunities and challenges in biomedical engineering education: focus on Bosnia and Herzegovina

D Bošković, A Badnjević - 2015 4th Mediterranean Conference …, 2015 - ieeexplore.ieee.org
Biomedical engineering employs engineering expertise in solving biological and medical
problems to improve the quality of life. The paper describes the significance of combining life …