Survey of machine learning algorithms for disease diagnostic
M Fatima, M Pasha - Journal of …, 2017 - geographical.openuniversityarchive …
In medical imaging, Computer Aided Diagnosis (CAD) is a rapidly growing dynamic area of
research. In recent years, significant attempts are made for the enhancement of computer …
research. In recent years, significant attempts are made for the enhancement of computer …
Machine learning in healthcare: A review
K Shailaja, B Seetharamulu… - … conference on electronics …, 2018 - ieeexplore.ieee.org
Machine Learning is modern and highly sophisticated technological applications became a
huge trend in the industry. Machine Learning is Omni present and is widely used in various …
huge trend in the industry. Machine Learning is Omni present and is widely used in various …
An IoMT‐Enabled Smart Healthcare Model to Monitor Elderly People Using Machine Learning Technique
The Internet of Medical Things (IoMT) enables digital devices to gather, infer, and broadcast
health data via the cloud platform. The phenomenal growth of the IoMT is fueled by many …
health data via the cloud platform. The phenomenal growth of the IoMT is fueled by many …
Metabolic syndrome and development of diabetes mellitus: predictive modeling based on machine learning techniques
The objective of this inductive research was to investigate: 1) the relationship between
diabetes mellitus and individual risk factors of metabolic syndrome (MetS), in a non …
diabetes mellitus and individual risk factors of metabolic syndrome (MetS), in a non …
[HTML][HTML] Education 4.0: teaching the basics of KNN, LDA and simple perceptron algorithms for binary classification problems
One of the main focuses of Education 4.0 is to provide students with knowledge on
disruptive technologies, such as Machine Learning (ML), as well as the skills to implement …
disruptive technologies, such as Machine Learning (ML), as well as the skills to implement …
Hybrid swarm intelligence algorithms with ensemble machine learning for medical diagnosis
Disease Diagnosis still an open problem in current research. The main characteristic of
diseases diagnostic model is that it helps physicians to make quick decisions and minimize …
diseases diagnostic model is that it helps physicians to make quick decisions and minimize …
Deep feature learning for disease risk assessment based on convolutional neural network with intra-layer recurrent connection by using hospital big data
This paper presents the analysis of real-life medical big data obtained from a hospital in
central China from 2013 to 2015 for risk assessment of cerebral infarction disease. We …
central China from 2013 to 2015 for risk assessment of cerebral infarction disease. We …
[PDF][PDF] A survey: detection and prediction of diabetes using machine learning techniques
P Indoria, YK Rathore - International Journal of Engineering …, 2018 - academia.edu
Diabetes is a one of the leading cause of blindness, kidney failure, amputations, heart failure
and stroke. When we eat, our body turns food into sugars, or glucose. At that point, our …
and stroke. When we eat, our body turns food into sugars, or glucose. At that point, our …
Ensemble method based predictive model for analyzing disease datasets: a predictive analysis approach
D Ramesh, YS Katheria - Health and Technology, 2019 - Springer
Medical datasets have attracted the research community for possible analysis and suitable
prediction, which helps the human to take proper precautions in preventing future diseases …
prediction, which helps the human to take proper precautions in preventing future diseases …
An elucidation for machine learning algorithms used in healthcare
V Kaur, R Kaur - Machine Learning for Edge Computing, 2022 - taylorfrancis.com
The world is moving rapidly towards the Artificial Intelligence arena. Artificial Intelligence is a
vast domain comprised of many significant subdomains. One among such popular domains …
vast domain comprised of many significant subdomains. One among such popular domains …