Related papers: Enhanced Classification Accuracy for Cardiotocogra…
The detection of cardiovascular diseases (CVD) using machine learning techniques represents a significant advancement in medical diagnostics, aiming to enhance early detection, accuracy, and efficiency. This study explores a comparative…
The use of machine learning algorithms in healthcare can amplify social injustices and health inequities. While the exacerbation of biases can occur and compound during the problem selection, data collection, and outcome definition, this…
Improving the precision of heart diseases detection has been investigated by many researchers in the literature. Such improvement induced by the overwhelming health care expenditures and erroneous diagnosis. As a result, various…
The amount of information in the form of features and variables avail- able to machine learning algorithms is ever increasing. This can lead to classifiers that are prone to overfitting in high dimensions, high di- mensional models do not…
Data mining is an important and challenging problem for the efficient analysis of large astronomical databases and will become even more important with the development of the Global Virtual Observatory. In this study, learning vector…
Multi-view echocardiographic sequences segmentation is crucial for clinical diagnosis. However, this task is challenging due to limited labeled data, huge noise, and large gaps across views. Here we propose a recurrent aggregation learning…
Support vector machines (SVM) and other kernel techniques represent a family of powerful statistical classification methods with high accuracy and broad applicability. Because they use all or a significant portion of the training data,…
An ensemble of classifiers combines several single classifiers to deliver a final prediction or classification decision. An increasingly provoking question is whether such systems can outperform the single best classifier. If so, what form…
In this work, we propose an ensemble of classifiers to distinguish between various degrees of abnormalities of the heart using Phonocardiogram (PCG) signals acquired using digital stethoscopes in a clinical setting, for the INTERSPEECH 2018…
In image classification, it is common practice to train deep networks to extract a single feature vector per input image. Few-shot classification methods also mostly follow this trend. In this work, we depart from this established direction…
Heart disease remains a leading cause of mortality and morbidity worldwide, necessitating the development of accurate and reliable predictive models to facilitate early detection and intervention. While state of the art work has focused on…
In recent times, the manufacturing processes are faced with many external or internal (the increase of customized product rescheduling , process reliability,..) changes. Therefore, monitoring and quality management activities for these…
Objective: A novel structure based on channel-wise attention mechanism is presented in this paper. Embedding with the proposed structure, an efficient classification model that accepts multi-lead electrocardiogram (ECG) as input is…
Investigation on the electrocardiogram (ECG) signals is an essential way to diagnose heart disease since the ECG process is noninvasive and easy to use. This work presents a supraventricular arrhythmia prediction model consisting of a few…
As the demand for internet of things (IoT) and device-to-device (D2D) applications in next generation communication systems increases, we are confronted with a challenge of spectrum scarcity. One promising solution to this problem is…
The merit of ensemble learning lies in having different outputs from many individual models on a single input, i.e., the diversity of the base models. The high quality of diversity can be achieved when each model is specialized to different…
The adoption of deep learning-based healthcare decision support systems such as the detection of irregular cardiac rhythm is hindered by challenges such as lack of access to quality data and the high costs associated with the collection and…
Incorporating feature selection into a classification or regression method often carries a number of advantages. In this paper we formalize feature selection specifically from a discriminative perspective of improving…
Ensemble methods are frequently used in recommender systems to improve accuracy by combining multiple models. Recent work reports sizable performance gains, but most studies still optimize primarily for accuracy and robustness rather than…
This study addresses the classification of heartbeats from ECG signals through two distinct approaches: traditional machine learning utilizing hand-crafted features and deep learning via transformed images of ECG beats. The dataset…