Comparison of Two-Criterion Evolutionary Filtering Techniques in Cardiovascular Predictive Modelling

Brester, Christina; Kauhanen, Jussi; Tuomainen, Tomi‐Pekka; Semenkin, ‪Eugene; Kolehmainen, Mikko

doi:10.5220/0005971101400145

Cited by 6 publications

(2 citation statements)

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“…There are some statistical criteria which might be optimized in the framework of the filter approach: Attribute Class Correlation (AC), Inter- and Intra- Class Distances (IE and IA), Laplasian Score (LS), and Representation Entropy (RE) [ 20 ]. Previously, we tested various combinations of these criteria and found the most appropriate two-objective model [ 21 ]: where is the j th example from the r th class, p is the central example of the data set, d (...,...) denotes the Euclidian distance, p r and n r represent the central example and the number of examples in the r th class. In our experiments, p and p r are assigned as vectors of average values of variables calculated on the whole set and on the r th class, respectively.…”

Section: Methodsmentioning

confidence: 99%

Evolutionary methods for variable selection in the epidemiological modeling of cardiovascular diseases

et al. 2018

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BackgroundThe redundancy of information is becoming a critical issue for epidemiologists. High-dimensional datasets require new effective variable selection methods to be developed. This study implements an advanced evolutionary variable selection method which is applied for cardiovascular predictive modeling. The epidemiological follow-up study KIHD (Kuopio Ischemic Heart Disease Risk Factor Study) was used to compare the designed variable selection method based on an evolutionary search with conventional stepwise selection. The sample contains in total 433 predictor variables and a response variable indicating incidents of cardiovascular diseases for 1465 study subjects.ResultsThe effectiveness of variable selection methods was investigated in combination with two models: Generalized Linear Logistic Regression and Support Vector Machine. We managed to decrease the number of variables from 433 to 38 and save the predictive ability of the models used. Their performance was evaluated with an F-score metric. At most, we gained 65.6% and 67.4% of the F-score before and after variable selection respectively. All the results were averaged over 5-folds of a cross-validation procedure.ConclusionsThe presented evolutionary variable selection method allows a reduced set of variables to be chosen which are relevant to predicting cardiovascular diseases. A reference list of the most meaningful variables is introduced to be used as a basis for new epidemiological studies. In general, the multicollinearity of variables enables different combinations of predictors to be used and the same performance of models to be attained.Electronic supplementary materialThe online version of this article (10.1186/s13040-018-0180-x) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Evolutionary methods for variable selection in the epidemiological modeling of cardiovascular diseases

et al. 2018

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“…After a search procedure in the space of possible feature subsets is defined and various subsets of features are generated, the evaluation of a specific subset of features is obtained by training and testing the targeted classification model. To search the space of all feature subsets, a search algorithm is wrapped around the classification model [14], [15].…”

Section: Background and Related Workmentioning

confidence: 99%

Smart Information Retrieval: Domain Knowledge Centric Optimization Approach

et al. 2019

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In the age of the Internet of Things, online data have witnessed a significant growth in terms of volume and diversity, and research into information retrieval has become one of the important research themes in the Internet-oriented data science research. This paper introduces a novel domain knowledge centric methodology aimed at improving the accuracy of using machine learning methods for relation extraction from text data, which is critical to the accuracy and efficiency of information retrievalbased applications, including recommender systems and sentiment analysis. The proposed methodology makes a significant contribution to the processes of domain knowledge-based relation extraction including interrogating Linked Open Datasets to generate the relation classification training data, addressing the imbalanced classification in the training datasets, determining the probability threshold of the best learning algorithm, and establishing the optimum parameters for genetic algorithms, which were utilized to optimize the feature selection for the learning algorithms. The experimental evaluation of the proposed methodology reveals that the adopted machine-learning algorithms exhibit higher precision and recall in relation extraction in the reduced feature space optimized by our implementation. The considered machine learning includes support vector machine, perceptron algorithm uneven margin, and K-nearest neighbors. The outcome is verified by comparing against the random mutation hill-climbing optimization algorithm using Wilcoxon signed-rank statistical analysis.

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Inverse Mathematical Modelling of Hexadecane Disintegration Reaction with Cooperative Multi-objective Genetic Algorithms

Ryzhikov

Brester

Semenkin

2019

Informatics in Control, Automation and Robotics

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Comparison of Two-Criterion Evolutionary Filtering Techniques in Cardiovascular Predictive Modelling

Cited by 6 publications

References 9 publications

Evolutionary methods for variable selection in the epidemiological modeling of cardiovascular diseases

Evolutionary methods for variable selection in the epidemiological modeling of cardiovascular diseases

Smart Information Retrieval: Domain Knowledge Centric Optimization Approach

Inverse Mathematical Modelling of Hexadecane Disintegration Reaction with Cooperative Multi-objective Genetic Algorithms

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