HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN

no code implementations • 6 Mar 2023 • Md Shofiqul Islam, Khondokar Fida Hasan, Sunjida Sultana, Shahadat Uddin, Pietro Lio, Julian M. W. Quinn, Mohammad Ali Moni

The experimental results demonstrate that the proposed HARDC model significantly outperforms other existing models, achieving an accuracy of 99. 60\%, F1 score of 98. 21\%, a precision of 97. 66\%, and recall of 99. 60\% using MIT-BIH generated ECG.

Denoising Generative Adversarial Network +1

Predicting Patient COVID-19 Disease Severity by means of Statistical and Machine Learning Analysis of Blood Cell Transcriptome Data

no code implementations • 19 Nov 2020 • Sakifa Aktar, Md. Martuza Ahamad, Md. Rashed-Al-Mahfuz, AKM Azad, Shahadat Uddin, A H M Kamal, Salem A. Alyami, Ping-I Lin, Sheikh Mohammed Shariful Islam, Julian M. W. Quinn, Valsamma Eapen, Mohammad Ali Moni

Introduction: For COVID-19 patients accurate prediction of disease severity and mortality risk would greatly improve care delivery and resource allocation.

Machine Learning and Meta-Analysis Approach to Identify Patient Comorbidities and Symptoms that Increased Risk of Mortality in COVID-19

no code implementations • 21 Aug 2020 • Sakifa Aktar, Ashis Talukder, Md. Martuza Ahamad, A. H. M. Kamal, Jahidur Rahman Khan, Md. Protikuzzaman, Nasif Hossain, Julian M. W. Quinn, Mathew A. Summers, Teng Liaw, Valsamma Eapen, Mohammad Ali Moni

COVID-19 patient comorbidities are likely to be informative about individual risk of severe illness and mortality.

BIG-bench Machine Learning