15. Computational health informatics using evolutionary-based feature selection

2020 ◽  
pp. 309-328
Keyword(s):  
Feature Selection ◽  
Health Informatics

Efficient Semi-Supervised Learning and Sparse Structural Learning for Feature Selection of Leukemia Dataset

2020 ◽  
Vol 10 (8) ◽  
pp. 1815-1824
Author(s):  
S. Nithya Roopa ◽  
N. Nagarajan
Keyword(s):  
Feature Selection ◽  
Supervised Learning ◽  
Health Informatics ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Research Work ◽  
Real Life ◽  
Support Vector ◽  
Structural Learning ◽  
Huge Amount

The amount of data produced in health informatics growing large and as a result analysis of this huge amount of data requires a great knowledge which is to be gained. The basic aim of health informatics is to take in real world medical data from all levels of human existence to help improve our understanding of medicine and medical practices. Huge amount of unlabeled data are obtainable in lots of real-life data-mining tasks, e.g., uncategorized messages in an automatic email categorization system, unknown genes functions for doing gene function calculation, and so on. Labelled data is frequently restricted and expensive to produce, while labelling classically needs human proficiency. Consequently, semi-supervised learning has become a topic of significant recent interest. This research work proposed a new semi-supervised grouping, where the performance of unsupervised clustering algorithms is enhanced with restricted numbers of supervision in labels form on constraints or data. The previous system designed a Clustering Guided Hybrid support vector machine based Sparse Structural Learning (CGHSSL) for feature selection. However, it does not produce a satisfactory accuracy results. In this research, proposed clustering-guided with Convolution Neural Network (CNN) based sparse structural learning clustering algorithm. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm is progressed for learning cluster labels of input samples having more accuracy guiding features election at same time. Concurrently, prediction of cluster labels is as well performed by CNN by means of using hidden structure which is shared by various characteristics. The parameters of CNN are then optimized maximizing Multi-objective Bee Colony (MBO) algorithm that can unravel feature correlations to render outcomes with additional consistency. Row-wise sparse designs are then balanced to yield design depicted to suit for feature selection. This semi supervised algorithm is utilized to choose important characteristics from Leukemia1 dataset additional resourcefully. Therefore dataset size is decreased significantly utilizing semi supervised algorithm prominently. As well proposed Semi Supervised Clustering-Guided Sparse Structural Learning (SSCGSSL) technique is utilized to increase the clustering performance in higher. The experimental results show that the proposed system achieves better performance compared with the existing system in terms of Accuracy, Entropy, Purity, Normalized Mutual Information (NMI) and F-measure.

Feature Selection Strategies and Perceptual Expertise in Configuration Search Tasks

2011 ◽  
Author(s):  
Lindsey M. Kitchell ◽  
Francisco J. Parada ◽  
Brandi L. Emerick ◽  
Tom A. Busey
Keyword(s):  
Feature Selection ◽  
Perceptual Expertise ◽  
Search Tasks ◽  
Selection Strategies

Health Informatics: Handle with Caution

1997 ◽  
Vol 36 (02) ◽  
pp. 79-81
Author(s):  
V. Leroy ◽  
S. Maurice-Tison ◽  
B. Le Blanc ◽  
R. Salamon
Keyword(s):  
Health Care ◽  
Data Management ◽  
Medical Informatics ◽  
Health Care Professionals ◽  
Data Security ◽  
Health Informatics ◽  
The Other ◽  
Inappropriate Use

Abstract:The increased use of computers is a response to the considerable growth in information in all fields of activities. Related to this, in the field of medicine a new component appeared about 40 years ago: Medical Informatics. Its goals are to assist health care professionals in the choice of data to manage and in the choice of applications of such data. These possibilities for data management must be well understood and, related to this, two major dangers must be emphasized. One concerns data security, and the other concerns the processing of these data. This paper discusses these items and warns of the inappropriate use of medical informatics.

Health Informatics – A Scientific Challenge?

1994 ◽  
Vol 33 (03) ◽  
pp. 250-253 ◽  
Author(s):  
J. R. Moehr
Keyword(s):  
Health Information ◽  
Health Informatics ◽  
Research And Teaching ◽  
Scientific Challenge

Abstract:The paper attempts to derive directions for research and teaching in health informatics. To this end, the achievements and continuing challenges of health informatics are exemplified, categorized, and related to common underlying phenomena. Suggestions by Blum and Blois are adopted which point to the complexity of health information as the critical ingredient. Examples are given of current efforts directed at dealing with this complexity. According to Popper and Brookes one may have to search for yet other ways of dealing specifically with information; we have barely started to explore these. It is suggested that this requirement for a fundamentally different orientation has profound consequences not only for our research but also for our teaching.

Health and Medical Informatics Education

1994 ◽  
Vol 33 (03) ◽  
pp. 246-249 ◽  
Author(s):  
R. Haux ◽  
F. J. Leven ◽  
J. R. Moehr ◽  
D. J. Protti
Keyword(s):  
Health Care ◽  
Health Information ◽  
Medical Informatics ◽  
Health Informatics ◽  
Information Science ◽  
Management Program ◽  
Science Program ◽  
Special Issue ◽  
Academic Degree ◽  
The University

Abstract:Health and medical informatics education has meanwhile gained considerable importance for medicine and for health care. Specialized programs in health/medical informatics have therefore been established within the last decades.This special issue of Methods of Information in Medicine contains papers on health and medical informatics education. It is mainly based on selected papers from the 5th Working Conference on Health/Medical Informatics Education of the International Medical Informatics Association (IMIA), which was held in September 1992 at the University of Heidelberg/Technical School Heilbronn, Germany, as part of the 20 years’ celebration of medical informatics education at Heidelberg/Heilbronn. Some papers were presented on the occasion of the 10th anniversary of the health information science program of the School of Health Information Science at the University of Victoria, British Columbia, Canada. Within this issue, programs in health/medical informatics are presented and analyzed: the medical informatics program at the University of Utah, the medical informatics program of the University of Heidelberg/School of Technology Heilbronn, the health information science program at the University of Victoria, the health informatics program at the University of Minnesota, the health informatics management program at the University of Manchester, and the health information management program at the University of Alabama. They all have in common that they are dedicated curricula in health/medical informatics which are university-based, leading to an academic degree in this field. In addition, views and recommendations for health/medical informatics education are presented. Finally, the question is discussed, whether health and medical informatics can be regarded as a separate discipline with the necessity for specialized curricula in this field.In accordance with the aims of IMIA, the intention of this special issue is to promote the further development of health and medical informatics education in order to contribute to high quality health care and medical research.

Health Informatics Education in the Third World

1989 ◽  
Vol 28 (04) ◽  
pp. 270-272 ◽  
Author(s):  
O. Rienhoff
Keyword(s):  
Developing Countries ◽  
Health Informatics ◽  
Third World ◽  
State Of The Art ◽  
The State ◽  
Collaboration Network ◽  
Educational Tools ◽  
The Third ◽  
The Third World

Abstract:The state of the art is summarized showing many efforts but only few results which can serve as demonstration examples for developing countries. Education in health informatics in developing countries is still mainly dealing with the type of health informatics known from the industrialized world. Educational tools or curricula geared to the matter of development are rarely to be found. Some WHO activities suggest that it is time for a collaboration network to derive tools and curricula within the next decade.

BLIND FEATURE SELECTION AND EXTRACTION IN A 3D IMAGE CUBE

2012 ◽  
Vol 19 (2) ◽  
pp. 97-111 ◽  
Author(s):  
Muhammad Ahmad ◽  
Syungyoung Lee ◽  
Ihsan Ul Haq ◽  
Qaisar Mushtaq
Keyword(s):  
Feature Selection ◽  
3D Image ◽  
Image Cube

An Enhancement of Feature Selection Algorithm for EDM: A Review

2018 ◽  
Vol 8 (5) ◽  
pp. 29
Author(s):  
Manpreet Kaur ◽  
Chamkaur Singh
Keyword(s):  
Feature Selection ◽  
Educational Data Mining ◽  
Problem Formulation ◽  
Research Area ◽  
Education Quality ◽  
Educational Institutions ◽  
Selection Algorithm ◽  
Positive Role ◽  
Data Set ◽  
Selection Algorithms

Educational Data Mining (EDM) is an emerging research area help the educational institutions to improve the performance of their students. Feature Selection (FS) algorithms remove irrelevant data from the educational dataset and hence increases the performance of classifiers used in EDM techniques. This paper present an analysis of the performance of feature selection algorithms on student data set. .In this papers the different problems that are defined in problem formulation. All these problems are resolved in future. Furthermore the paper is an attempt of playing a positive role in the improvement of education quality, as well as guides new researchers in making academic intervention.

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