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A review of data mining using big data in health informatics
The amount of data produced within Health Informatics has grown to be quite vast, and
analysis of this Big Data grants potentially limitless possibilities for knowledge to be gained …
analysis of this Big Data grants potentially limitless possibilities for knowledge to be gained …
Computational health informatics in the big data age: a survey
The explosive growth and widespread accessibility of digital health data have led to a surge
of research activity in the healthcare and data sciences fields. The conventional approaches …
of research activity in the healthcare and data sciences fields. The conventional approaches …
Potential value and impact of data mining and machine learning in clinical diagnostics
Data mining involves the use of mathematical sciences, statistics, artificial intelligence, and
machine learning to determine the relationships between variables from a large sample of …
machine learning to determine the relationships between variables from a large sample of …
Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients
SM Vieira, LF Mendonça, GJ Farinha… - Applied Soft Computing, 2013 - Elsevier
This paper proposes a modified binary particle swarm optimization (MBPSO) method for
feature selection with the simultaneous optimization of SVM kernel parameter setting …
feature selection with the simultaneous optimization of SVM kernel parameter setting …
A comprehensive review on smart health care: applications, paradigms, and challenges with case studies
Growth and advancement of the Deep Learning (DL) and the Internet of Things (IoT) are
figuring out their way over the modern contemporary world through integrating various …
figuring out their way over the modern contemporary world through integrating various …
An improved support vector machine-based diabetic readmission prediction
Background and objective In healthcare systems, the cost of unplanned readmission
accounts for a large proportion of total hospital payment. Hospital-specific readmission rate …
accounts for a large proportion of total hospital payment. Hospital-specific readmission rate …
Machine learning approaches in smart health
The increase of age average led to an increase in the demand of providing and improving
the service of healthcare. The advancing of the information and communication technology …
the service of healthcare. The advancing of the information and communication technology …
Predicting ICU readmission using grouped physiological and medication trends
Background Patients who are readmitted to an intensive care unit (ICU) usually have a high
risk of mortality and an increased length of stay. ICU readmission risk prediction may help …
risk of mortality and an increased length of stay. ICU readmission risk prediction may help …
[HTML][HTML] An integrated data mining algorithms and meta-heuristic technique to predict the readmission risk of diabetic patients
M Zeinalnezhad, S Shishehchi - Healthcare Analytics, 2024 - Elsevier
Reducing hospital readmission rate is a significant challenge in the healthcare industry for
managers and policymakers seeking to improve healthcare and lower costs. This study …
managers and policymakers seeking to improve healthcare and lower costs. This study …
Log correlation for intrusion detection: A proof of concept
Intrusion detection is an important part of networked-systems security protection. Although
commercial products exist, finding intrusions has proven to be a difficult task with limitations …
commercial products exist, finding intrusions has proven to be a difficult task with limitations …