Detecting congestive heart failure by extracting multimodal features and employing machine learning techniques

L Hussain, IA Awan, W Aziz, S Saeed… - BioMed research …, 2020 - Wiley Online Library
The adaptability of heart to external and internal stimuli is reflected by the heart rate
variability (HRV). Reduced HRV can be a predictor of negative cardiovascular outcomes …

[HTML][HTML] Artificial Intelligence based accurately load forecasting system to forecast short and medium-term load demands

FM Butt, L Hussain, A Mahmood… - Mathematical Biosciences …, 2021 - aimspress.com
An efficient management and better scheduling by the power companies are of great
significance for accurate electrical load forecasting. There exists a high level of uncertainties …

An improved method to detect arrhythmia using ensemble learning-based model in multi lead electrocardiogram (ECG)

S Mandala, A Rizal, Adiwijaya, S Nurmaini… - Plos one, 2024 - journals.plos.org
Arrhythmia is a life-threatening cardiac condition characterized by irregular heart rhythm.
Early and accurate detection is crucial for effective treatment. However, single-lead …

Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI

L Hussain, AA Malibari, JS Alzahrani, M Alamgeer… - Scientific Reports, 2022 - nature.com
Accurate classification of brain tumor subtypes is important for prognosis and treatment.
Researchers are develo** tools based on static and dynamic feature extraction and …

[PDF][PDF] Machine learning based congestive heart failure detection using feature importance ranking of multimodal features

L Hussain, W Aziz, IR Khan, MH Alkinani… - Math Biosci …, 2021 - pdfs.semanticscholar.org
In this study, we ranked the Multimodal Features extracted from Congestive Heart Failure
(CHF) and Normal Sinus Rhythm (NSR) subjects. We categorized the ranked features into 1 …

Detecting congestive heart failure by extracting multimodal features with synthetic minority oversampling technique (SMOTE) for imbalanced data using robust …

L Hussain, KJ Lone, IA Awan, AA Abbasi… - Waves in Random and …, 2022 - Taylor & Francis
The incidence of congestive heart failure (CHF) is approximately 10 per 1000 for Americans
over the age of 65 years. The dynamics of CHF are highly complex, nonlinear, and temporal …

Analyzing the dynamics of lung cancer imaging data using refined fuzzy entropy methods by extracting different features

L Hussain, W Aziz, AA Alshdadi, MSA Nadeem… - IEEE …, 2019 - ieeexplore.ieee.org
Lung cancer is the major cause of cancer-related deaths worldwide with poor survival due to
the poor diagnostic system at the advanced cancer stage. In the past, researchers …

[PDF][PDF] Unveiling the power of convolutional networks: Applied computational intelligence for arrhythmia detection from ECG signals

AS Aziz, HK Mohamed… - Journal of International …, 2022 - researchgate.net
Arrhythmias are a significant cause of morbidity and mortality worldwide, necessitating
accurate and timely detection for effective clinical intervention. Electrocardiogram (ECG) …

Deep convolutional neural networks accurately predict breast cancer using mammograms

L Hussain, S Ansari, M Shabir, SA Qureshi… - Waves in Random …, 2023 - Taylor & Francis
Breast cancer in women is the most frequently diagnosed and major leading cause of
cancer deaths. Due to the complex nature of microcalcification and masses, radiologists fail …