Success/Failure Prediction of Noninvasive Mechanical Ventilation in Intensive Care Units

2016 ◽  
Vol 55 (03) ◽  
pp. 234-241 ◽  
Author(s):  
Félix Martín-González ◽  
Javier González-Robledo ◽  
Fernando Sánchez-Hernández ◽  
María Moreno-García
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Influential Factors ◽  
Selection Methods ◽  
Noninvasive Mechanical Ventilation ◽  
Mining Methods ◽  
The One ◽  
Feature Selection Techniques

SummaryObjectives: This paper addresses the problem of decision-making in relation to the administration of noninvasive mechanical ventila tion (NIMV) in intensive care units.Methods: Data mining methods were employed to find out the factors influencing the success/failure of NIMV and to predict its results in future patients. These artificial intelligence-based methods have not been applied in this field in spite of the good results obtained in other medical areas.Results: Feature selection methods provided the most influential variables in the success/ failure of NIMV, such as NIMV hours, PaCO2 at the start, PaO2 / FiO2 ratio at the start, hematocrit at the start or PaO2 / FiO2 ratio after two hours. These methods were also used in the preprocessing step with the aim of improving the results of the classifiers. The algorithms provided the best results when the dataset used as input was the one containing the attributes selected with the CFS method. Conclusions: Data mining methods can be successfully applied to determine the most influential factors in the success/failure of NIMV and also to predict NIMV results in future patients. The results provided by classifiers can be improved by preprocessing the data with feature selection techniques.

scholarly journals Prediction of mortality in Intensive Care Units: a multivariate feature selection

2020 ◽  
Vol 107 ◽  
pp. 103456
Author(s):  
Flávio Monteiro ◽  
Fernando Meloni ◽  
José Augusto Baranauskas ◽  
Alessandra Alaniz Macedo
Keyword(s):  
Feature Selection ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Prediction Of Mortality

CLASSIFICATION OF HIGH-DIMENSIONAL MICROARRAY DATA WITH A TWO-STEP PROCEDURE VIA A WILCOXON CRITERION AND MULTILAYER PERCEPTRON

2011 ◽  
Vol 10 (01) ◽  
pp. 1-14
Author(s):  
VLADIMIR NIKULIN ◽  
TIAN-HSIANG HUANG ◽  
GEOFFREY J. MCLACHLAN
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
High Dimensional ◽  
Second Step ◽  
Support Vector ◽  
Step Procedure ◽  
Leave One Out ◽  
Natural Combination ◽  
Feature Selection Techniques

The method presented in this paper is novel as a natural combination of two mutually dependent steps. Feature selection is a key element (first step) in our classification system, which was employed during the 2010 International RSCTC data mining (bioinformatics) Challenge. The second step may be implemented using any suitable classifier such as linear regression, support vector machine or neural networks. We conducted leave-one-out (LOO) experiments with several feature selection techniques and classifiers. Based on the LOO evaluations, we decided to use feature selection with the separation type Wilcoxon-based criterion for all final submissions. The method presented in this paper was tested successfully during the RSCTC data mining Challenge, where we achieved the top score in the Basic track.

scholarly journals Automated Feature Selection and Classification for High-Dimensional Biomedical Data

2021 ◽  
Author(s):  
Tammo P.A. Beishuizen ◽  
Joaquin Vanschoren ◽  
Peter A.J. Hilbers ◽  
Dragan Bošnački
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Automated System ◽  
Complex Data ◽  
Biomedical Data ◽  
Selection Methods ◽  
Model Predictions ◽  
Automated Machine Learning ◽  
Feature Selection Techniques ◽  
Best Fit

Abstract Background: Automated machine learning aims to automate the building of accurate predictive models, including the creation of complex data preprocessing pipelines. Although successful in many fields, they struggle to produce good results on biomedical datasets, especially given the high dimensionality of the data. Result: In this paper, we explore the automation of feature selection in these scenarios. We analyze which feature selection techniques are ideally included in an automated system, determine how to efficiently find the ones that best fit a given dataset, integrate this into an existing AutoML tool (TPOT), and evaluate it on four very different yet representative types of biomedical data: microarray, mass spectrometry, clinical and survey datasets. We focus on feature selection rather than latent feature generation since we often want to explain the model predictions in terms of the intrinsic features of the data. Conclusion: Our experiments show that for none of these datasets we need more than 200 features to accurately explain the output. Additional features did not increase the quality significantly. We also find that the automated machine learning results are significantly improved after adding additional feature selection methods and prior knowledge on how to select and tune them.

scholarly journals A Survey on Phishing Detection and The Importance of Feature Selection In Data Mining Classification Algorithms

2020 ◽  
pp. 11-18
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Support Vector ◽  
Classification Algorithms ◽  
End User ◽  
Preparation Methods ◽  
Survey Paper ◽  
Vector Machines ◽  
Feature Selection Techniques ◽  
Phishing Detection

: In this era of Internet, the issue of security of information is at its peak. One of the main threats in this cyber world is phishing attacks which is an email or website fraud method that targets the genuine webpage or an email and hacks it without the consent of the end user. There are various techniques which help to classify whether the website or an email is legitimate or fake. The major contributors in the process of detection of these phishing frauds include the classification algorithms, feature selection techniques or dataset preparation methods and the feature extraction that plays an important role in detection as well as in prevention of these attacks. This Survey Paper studies the effect of all these contributors and the approaches that are applied in the study conducted on the recent papers. Some of the classification algorithms that are implemented includes Decision tree, Random Forest , Support Vector Machines, Logistic Regression , Lazy K Star, Naive Bayes and J48 etc.

An Optimal Categorization of Feature Selection Methods for Knowledge Discovery

Data Mining ◽  
2013 ◽  
pp. 92-106
Author(s):  
Harleen Kaur ◽  
Ritu Chauhan ◽  
M. Alam
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Discriminant Analysis ◽  
Medical Data ◽  
Stepwise Discriminant Analysis ◽  
Selection Methods ◽  
Medical Databases ◽  
Active Research ◽  
Potential Improvement ◽  
Large Effort

With the continuous availability of massive experimental medical data has given impetus to a large effort in developing mathematical, statistical and computational intelligent techniques to infer models from medical databases. Feature selection has been an active research area in pattern recognition, statistics, and data mining communities. However, there have been relatively few studies on preprocessing data used as input for data mining systems in medical data. In this chapter, the authors focus on several feature selection methods as to their effectiveness in preprocessing input medical data. They evaluate several feature selection algorithms such as Mutual Information Feature Selection (MIFS), Fast Correlation-Based Filter (FCBF) and Stepwise Discriminant Analysis (STEPDISC) with machine learning algorithm naive Bayesian and Linear Discriminant analysis techniques. The experimental analysis of feature selection technique in medical databases has enable the authors to find small number of informative features leading to potential improvement in medical diagnosis by reducing the size of data set, eliminating irrelevant features, and decreasing the processing time.

Analysis of flight delays in aviation system using different classification algorithms and feature selection methods

2019 ◽  
Vol 123 (1267) ◽  
pp. 1415-1436 ◽  
Author(s):  
A. B. A. Anderson ◽  
A. J. Sanjeev Kumar ◽  
A. B. Arockia Christopher
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Classification Model ◽  
System Level ◽  
Support Vector ◽  
Flight Delays ◽  
Data Mining Techniques ◽  
Mining Methods ◽  
Artificial Neural Network Ann ◽  
Aircraft System

ABSTRACTData mining is a process of finding correlations and collecting and analysing a huge amount of data in a database to discover patterns or relationships. Flight delay creates significant problems in the present aviation system. Data mining techniques are desired for analysing the performance in which micro-level causes propagate to make system-level patterns of delay. Analysing flight delays is very difficult – both when looking from a historical view as well as when estimating delays with forecast demand. This paper proposes using Decision Tree (DT), Support Vector Machine (SVM), Naive Bayesian (NB), K-nearest neighbour (KNN) and Artificial Neural Network (ANN) to study and analyse delays among aircrafts. The performance of different data mining methods is found in the different regions of the updated datasets on these classifiers. Finally, the result shows a significant variation in the performance of different data mining methods and feature selection for this problem. This paper aims to deal with how data mining techniques can be used to understand difficult aircraft system delays in aviation. Our aim is to develop a classification model for studying and reducing delay using different data mining methods and, in this manner, to show that DT has a greater classification accuracy. The different feature selectors are used in this study in order to reduce the number of initial attributes. Our results clearly demonstrate the value of DT for analysing and visualising how system-level effects happen from subsystem-level causes.

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