A review of feature selection methods for machine learning-based disease risk prediction
N Pudjihartono, T Fadason, AW Kempa-Liehr… - Frontiers in …, 2022 - frontiersin.org
Machine learning has shown utility in detecting patterns within large, unstructured, and
complex datasets. One of the promising applications of machine learning is in precision …
complex datasets. One of the promising applications of machine learning is in precision …
A comprehensive survey on sentiment analysis: Approaches, challenges and trends
Sentiment analysis (SA), also called Opinion Mining (OM) is the task of extracting and
analyzing people's opinions, sentiments, attitudes, perceptions, etc., toward different entities …
analyzing people's opinions, sentiments, attitudes, perceptions, etc., toward different entities …
A comprehensive survey on recent metaheuristics for feature selection
Feature selection has become an indispensable machine learning process for data
preprocessing due to the ever-increasing sizes in actual data. There have been many …
preprocessing due to the ever-increasing sizes in actual data. There have been many …
A deep look into radiomics
C Scapicchio, M Gabelloni, A Barucci, D Cioni… - La radiologia …, 2021 - Springer
Radiomics is a process that allows the extraction and analysis of quantitative data from
medical images. It is an evolving field of research with many potential applications in …
medical images. It is an evolving field of research with many potential applications in …
Heart disease identification method using machine learning classification in e-healthcare
Heart disease is one of the complex diseases and globally many people suffered from this
disease. On time and efficient identification of heart disease plays a key role in healthcare …
disease. On time and efficient identification of heart disease plays a key role in healthcare …
A comprehensive survey on feature selection in the various fields of machine learning
Abstract In Machine Learning (ML), Feature Selection (FS) plays a crucial part in reducing
data's dimensionality and enhancing any proposed framework's performance. However, in …
data's dimensionality and enhancing any proposed framework's performance. However, in …
Deep learning for anomaly detection: A survey
R Chalapathy, S Chawla - arXiv preprint arXiv:1901.03407, 2019 - arxiv.org
Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …
research areas and application domains. The aim of this survey is two-fold, firstly we present …
A hybrid intelligent system framework for the prediction of heart disease using machine learning algorithms
Heart disease is one of the most critical human diseases in the world and affects human life
very badly. In heart disease, the heart is unable to push the required amount of blood to …
very badly. In heart disease, the heart is unable to push the required amount of blood to …
COVID-Classifier: An automated machine learning model to assist in the diagnosis of COVID-19 infection in chest x-ray images
Chest-X ray (CXR) radiography can be used as a first-line triage process for non-COVID-19
patients with pneumonia. However, the similarity between features of CXR images of COVID …
patients with pneumonia. However, the similarity between features of CXR images of COVID …
POSREG: proteomic signature discovered by simultaneously optimizing its reproducibility and generalizability
Mass spectrometry-based proteomic technique has become indispensable in current
exploration of complex and dynamic biological processes. Instrument development has …
exploration of complex and dynamic biological processes. Instrument development has …