Object tracking: A survey
The goal of this article is to review the state-of-the-art tracking methods, classify them into
different categories, and identify new trends. Object tracking, in general, is a challenging …
different categories, and identify new trends. Object tracking, in general, is a challenging …
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 …
Enhancing heart disease prediction accuracy through machine learning techniques and optimization
In the medical domain, early identification of cardiovascular issues poses a significant
challenge. This study enhances heart disease prediction accuracy using machine learning …
challenge. This study enhances heart disease prediction accuracy using machine learning …
A hybrid method for heart disease diagnosis utilizing feature selection based ensemble classifier model generation
Heart disease is one of the most complicated diseases, and it affects a large number of
individuals throughout the world. In healthcare, particularly cardiology, early and accurate …
individuals throughout the world. In healthcare, particularly cardiology, early and accurate …
[HTML][HTML] A Random Forest based predictor for medical data classification using feature ranking
Medical data classification is considered to be a challenging task in the field of medical
informatics. Although many works have been reported in the literature, there is still scope for …
informatics. Although many works have been reported in the literature, there is still scope for …
MIFS-ND: A mutual information-based feature selection method
Feature selection is used to choose a subset of relevant features for effective classification of
data. In high dimensional data classification, the performance of a classifier often depends …
data. In high dimensional data classification, the performance of a classifier often depends …
An optimized feature selection based on genetic approach and support vector machine for heart disease
Heart disease diagnosis is found to be a challenging issue which can offer a computerized
estimate about the level of heart disease so that supplementary action can be made easy …
estimate about the level of heart disease so that supplementary action can be made easy …
The effect of training and testing process on machine learning in biomedical datasets
Training and testing process for the classification of biomedical datasets in machine learning
is very important. The researcher should choose carefully the methods that should be used …
is very important. The researcher should choose carefully the methods that should be used …
Computational intelligence for heart disease diagnosis: A medical knowledge driven approach
This paper investigates a number of computational intelligence techniques in the detection
of heart disease. Particularly, comparison of six well known classifiers for the well used …
of heart disease. Particularly, comparison of six well known classifiers for the well used …
A hybrid classification system for heart disease diagnosis based on the RFRS method
Heart disease is one of the most common diseases in the world. The objective of this study is
to aid the diagnosis of heart disease using a hybrid classification system based on the …
to aid the diagnosis of heart disease using a hybrid classification system based on the …