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Data preprocessing for heart disease classification: A systematic literature review
Context Early detection of heart disease is an important challenge since 17.3 million people
yearly lose their lives due to heart diseases. Besides, any error in diagnosis of cardiac …
yearly lose their lives due to heart diseases. Besides, any error in diagnosis of cardiac …
[HTML][HTML] Comparing different supervised machine learning algorithms for disease prediction
Supervised machine learning algorithms have been a dominant method in the data mining
field. Disease prediction using health data has recently shown a potential application area …
field. Disease prediction using health data has recently shown a potential application area …
A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks
Abstract Wireless Body Sensor Network (BSNs) are wearable sensors with varying sensing,
storage, computation, and transmission capabilities. When data is obtained from multiple …
storage, computation, and transmission capabilities. When data is obtained from multiple …
[HTML][HTML] Classification of arrhythmia by using deep learning with 2-D ECG spectral image representation
The electrocardiogram (ECG) is one of the most extensively employed signals used in the
diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture …
diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture …
A novel breast cancer detection architecture based on a CNN-CBR system for mammogram classification
This paper presents a novel framework for breast cancer detection using mammogram
images. The proposed solution aims to output an explainable classification from a …
images. The proposed solution aims to output an explainable classification from a …
A systematic map of medical data preprocessing in knowledge discovery
Background and objective Datamining (DM) has, over the last decade, received increased
attention in the medical domain and has been widely used to analyze medical datasets in …
attention in the medical domain and has been widely used to analyze medical datasets in …
An optimized machine learning framework for predicting intradialytic hypotension using indexes of chronic kidney disease-mineral and bone disorders
Intradialytic hypotension (IDH) is the most common acute complication in hemodialysis (HD)
sessions and is associated with increased morbidity and mortality in HD patients. To prevent …
sessions and is associated with increased morbidity and mortality in HD patients. To prevent …
An IoT based efficient hybrid recommender system for cardiovascular disease
A fog-based IoT model can be helpful for patients from remote areas with cardiovascular
disease. An expert cardiologist is usually not available in such remote areas. There are …
disease. An expert cardiologist is usually not available in such remote areas. There are …
Arrhythmia classification of ECG signals using hybrid features
Automatic detection and classification of life‐threatening arrhythmia plays an important part
in dealing with various cardiac conditions. In this paper, a novel method for classification of …
in dealing with various cardiac conditions. In this paper, a novel method for classification of …
Multiclass classification of cardiac arrhythmia using improved feature selection and SVM invariants
Arrhythmia is considered a life‐threatening disease causing serious health issues in
patients, when left untreated. An early diagnosis of arrhythmias would be helpful in saving …
patients, when left untreated. An early diagnosis of arrhythmias would be helpful in saving …