Data preprocessing for heart disease classification: A systematic literature review
H Benhar, A Idri, JL Fernández-Alemán - Computer Methods and Programs …, 2020 - Elsevier
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 …
Automatic sleep stage classification: From classical machine learning methods to deep learning
RN Sekkal, F Bereksi-Reguig… - … Signal Processing and …, 2022 - Elsevier
Background and objectives The classification of sleep stages is a preliminary exam that
contributes to the diagnosis of possible sleep disorders. However, it is a tedious and time …
contributes to the diagnosis of possible sleep disorders. However, it is a tedious and time …
Prediction of chronic kidney disease-a machine learning perspective
P Chittora, S Chaurasia, P Chakrabarti… - IEEE …, 2021 - ieeexplore.ieee.org
Chronic Kidney Disease is one of the most critical illness nowadays and proper diagnosis is
required as soon as possible. Machine learning technique has become reliable for medical …
required as soon as possible. Machine learning technique has become reliable for medical …
A new ensemble-based intrusion detection system for internet of things
The domain of Internet of Things (IoT) has witnessed immense adaptability over the last few
years by drastically transforming human lives to automate their ordinary daily tasks. This is …
years by drastically transforming human lives to automate their ordinary daily tasks. This is …
A deep learning approach based on convolutional LSTM for detecting diabetes
Diabetes is a chronic disease that occurs when the pancreas does not generate sufficient
insulin or the body cannot effectively utilize the produced insulin. If it remains unidentified …
insulin or the body cannot effectively utilize the produced insulin. If it remains unidentified …
A systematic method for breast cancer classification using RFE feature selection
RK Sachdeva, P Bathla, P Rani… - 2022 2nd …, 2022 - ieeexplore.ieee.org
Breast cancer is among leading reasons for the deaths of women globally. Machine learning
techniques can help to classify breast cancer based on some features. In order to find a …
techniques can help to classify breast cancer based on some features. In order to find a …
Multimodal depression detection: fusion analysis of paralinguistic, head pose and eye gaze behaviors
An estimated 350 million people worldwide are affected by depression. Using affective
sensing technology, our long-term goal is to develop an objective multimodal system that …
sensing technology, our long-term goal is to develop an objective multimodal system that …
A random forest classifier for lymph diseases
AT Azar, HI Elshazly, AE Hassanien… - Computer methods and …, 2014 - Elsevier
Abstract Machine learning-based classification techniques provide support for the decision-
making process in many areas of health care, including diagnosis, prognosis, screening, etc …
making process in many areas of health care, including diagnosis, prognosis, screening, etc …
Deep learning approaches for bad smell detection: a systematic literature review
Context Bad smells negatively impact software quality metrics such as understandability,
reusability, and maintainability. Reduced costs and enhanced software quality can be …
reusability, and maintainability. Reduced costs and enhanced software quality can be …
Automatic disease detection of basal stem rot using deep learning and hyperspectral imaging
Basal Stem Rot (BSR), a disease caused by Ganoderma boninense (G. boninense), has
posed a significant concern for the oil palm industry, particularly in Southeast Asia, as it has …
posed a significant concern for the oil palm industry, particularly in Southeast Asia, as it has …