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

Christina Brester, Jussi Kauhanen, Tomi-Pekka Tuomainen, Eugene Semenkin, Mikko Kolehmainen
2016 Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics  
Comparison of Two-Criterion Evolutionary Filtering Techniques in Cardiovascular Predictive Modelling.  ...  In this paper we compare a number of two-criterion filtering techniques for feature selection in cardiovascular predictive modelling.  ...  CONCLUSIONS In this paper we introduced a number of two-criterion filtering techniques as a feature selection tool in the predictive modelling of cardiovascular diseases.  ... 
doi:10.5220/0005971101400145 dblp:conf/icinco/BresterKTSK16 fatcat:5bzmrrhnxbbcljiudepwwgriea

Prediction system for heart disease using Naive Bayes and particle swarm optimization

Uma N Dulhare
2018 Biomedical Research  
One of data mining technique as classification is a supervised learning used to accurately predict the target class for each case in the data.  ...  Experimental result shows that the proposed model with PSO as feature selection increases the predictive accuracy of the Naive Bayes to classify heart disease.  ...  Particle swarm optimization (PSO) PSO is an Evolutionary Computation technique is proposed by Kennedy et al. in 1995 [8] .  ... 
doi:10.4066/biomedicalresearch.29-18-620 fatcat:kksylz4chbgmvkewj4jfrtkkbu

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

Christina Brester, Jussi Kauhanen, Tomi-Pekka Tuomainen, Sari Voutilainen, Mauno Rönkkö, Kimmo Ronkainen, Eugene Semenkin, Mikko Kolehmainen
2018 BioData Mining  
This study implements an advanced evolutionary variable selection method which is applied for cardiovascular predictive modeling.  ...  Results: The effectiveness of variable selection methods was investigated in combination with two models: Generalized Linear Logistic Regression and Support Vector Machine.  ...  The performance of cardiovascular predictive modeling in combination with variable selections.  ... 
doi:10.1186/s13040-018-0180-x pmid:30127856 pmcid:PMC6092817 fatcat:hpsmwiz56vdl5dzs4c3m7yazee

Combining Physiology-Based Modeling and Evolutionary Algorithms for Personalized, Noninvasive Cardiovascular Assessment Based on Electrocardiography and Ballistocardiography

Nicholas Mattia Marazzi, Giovanna Guidoboni, Mohamed Zaid, Lorenzo Sala, Salman Ahmad, Laurel Despins, Mihail Popescu, Marjorie Skubic, James Keller
2022 Frontiers in Physiology  
monitoring, (ii) a physiology-based mathematical model for predicting personalized cardiovascular variables, and (iii) an evolutionary algorithm (EA) for searching optimal model parameters.Methods: Electrocardiogram  ...  The time distance between R peaks is used as an input for a validated physiology-based mathematical model that predicts distributions of pressures and volumes in the cardiovascular system, along with the  ...  ACKNOWLEDGMENTS The authors acknowledge support from the Center of Eldercare and Rehabilitation Technology and the University of Missouri.  ... 
doi:10.3389/fphys.2021.739035 pmid:35095545 pmcid:PMC8790319 fatcat:wknyc4ibc5ctpbt7eokrv5pmna

Evolutionary Techniques for Optimizing Air Quality Model

Rashmi Bhardwaj, Dimple Pruthi
2020 Procedia Computer Science  
The study contributes to the advancing research by examining pertinence of evolutionary algorithms-particle swarm optimization (PSO) and genetic algorithm (GA) with ANFIS in predicting fine particulate  ...  In this study, the adaptive neuro-fuzzy inference system (ANFIS) is used to perform predictive analysis of air pollutant-fine particulate matter.  ...  Evaluation Criterion The performance of the proposed model is evaluated and compared to existing models.  ... 
doi:10.1016/j.procs.2020.03.206 fatcat:fvnidre7azhjxeoakduxzvdn6m

Evolutionary Machine Learning: A Survey

Akbar Telikani, Amirhessam Tahmassebi, Wolfgang Banzhaf, Amir H. Gandomi
2022 ACM Computing Surveys  
EC algorithms have recently been used to improve the performance of Machine Learning (ML) models and the quality of their results.  ...  For each category, we discuss evolutionary machine learning in terms of three aspects: problem formulation, search mechanisms, and fitness value computation.  ...  The evolutionary ARM is one of the techniques used in collaborative filtering in which user preferences for items of interest are expressed as ratings.  ... 
doi:10.1145/3467477 fatcat:o6m3nekqfnaudjnxxoeferhine

Integrated Gabor Filter and Trilateral Filter for Exudate Extraction in Fundus Images

Kanika Bajaj, Navjot Kaur
2017 International Journal of Image Graphics and Signal Processing  
Lack of accuracy in these techniques can lead to fatal results because of incorrect treatment.  ...  Therefore to improve the accuracy of exudate extraction further a Hybrid Gabor filter bank with trilateral based filtering technique is proposed.  ...  The experimental results are dependent upon two eye databases and prove that this technique can achieve a better performance in comparison to other algorithms.  ... 
doi:10.5815/ijigsp.2017.01.02 fatcat:ez5htsbbrjbr7gi2wqnffoqnly

Computational Methods for the Discovery of Metabolic Markers of Complex Traits

Michael Lee, Ting Hu
2019 Metabolites  
With an upward trend in the use of highly-accurate, multivariate models in the metabolomics literature, diagnostic biomarker panels of complex diseases are more recently achieving accuracies approaching  ...  In this review, the workflow of metabolic marker discovery is outlined from metabolite extraction to model interpretation and validation.  ...  Mutations can be the alteration of elements of a symbolic model, and recombination swaps sections of two symbolic models in the hope of producing better child models.  ... 
doi:10.3390/metabo9040066 pmid:30987289 pmcid:PMC6523328 fatcat:iluqijb2pvgytkdc2m4gjmdtia

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

Kwok Chui, Wadee Alhalabi, Sally Pang, Patricia Pablos, Ryan Liu, Mingbo Zhao
2017 Sustainability  
Owning to the fact that there are plenty of applications in healthcare, four applications in the field of diseases diagnosis (which also list in the top 10 causes of global death in 2015), namely cardiovascular  ...  In addition, challenges in the deployment of disease diagnosis in healthcare have been discussed.  ...  Author Contributions: All of the authors have contributed to the drafting of manuscript. The idea and structure of this manuscript was proposed by Kwok Tai Chui.  ... 
doi:10.3390/su9122309 fatcat:7tmwfunpo5a4baqyc73gqkqggy

Predicting COVID-19 hospitalizations with attribute selection based on genetic and classification algorithms

Miriam Pizzatto Colpo, Bruno Cascaes Alves, Kevin Soares Pereira, Anna Flávia Zimmermann Brandão, Marilton Sanchotene de Aguiar, Tiago Thompsen Primo
2022 iSys  
However, as the excess of information, often irrelevant or redundant, can impair predictive models' performance, we propose a hybrid approach to attribute selection in this work.  ...  This method aims to find an optimal attribute subset through a genetic algorithm, which considers the results of a classification model in its evaluation function to improve the hospitalization need prediction  ...  The method surpassed other evolutionary algorithms and more traditional selection techniques by being evaluated using different disease databases in predictive tasks.  ... 
doi:10.5753/isys.2022.2187 fatcat:iwhujnvf7bc4vlgrx5d5jhbq2i

Ensemble Positive Unlabeled Learning for Disease Gene Identification

Peng Yang, Xiaoli Li, Hon-Nian Chua, Chee-Keong Kwoh, See-Kiong Ng, Enrique Hernandez-Lemus
2014 PLoS ONE  
Through integrating multiple biological data sources for training and the outputs of an ensemble of PU learning classifiers for prediction, we are able to minimize the potential bias and errors in individual  ...  Using only a single source of data for prediction can be susceptible to bias due to incompleteness and noise in the genomic data and a single machine learning predictor prone to bias caused by inherent  ...  Therefore, we apply logistic function to filter out low phenotypic similarities in E PH , following [2] [8] . The proposed technique EPU The schema of our EPU algorithm is presented in Figure 1 .  ... 
doi:10.1371/journal.pone.0097079 pmid:24816822 pmcid:PMC4016241 fatcat:jt4z5li7jfcc7an2ybx2jyhmwu

Training of Multilayer Perceptrons with Improved Particle Swarm Optimization for the Heart Diseases Prediction

Kelwade JP, Salankar SS
2017 International Journal of Swarm Intelligence and Evolutionary Computation  
This paper suggests a new approach for enhancement of the prediction accuracy of Multi-Layer Perceptrons (MLP) neural network using improved Particle Swarm Optimization (IPSO) technique.  ...  Inte rn a ti o na l Jo urnal of S w a r m In tellig enc e an d E v ol utionary C om p ut at ion Abstract The study of Heart rate variability is recently gained momentum for an estimation of heart health  ...  The filtering of ECG signals is performed with bandpass filter to remove powerline interferences. Pan and Tompkins algorithm for detection of QRS complexes and then R peaks is utilized in this study.  ... 
doi:10.4172/2090-4908.1000156 fatcat:mpt46fqek5cxvadlybxewioz4u

GSVMA: A Genetic-Support Vector Machine-Anova method for CAD diagnosis based on Z-Alizadeh Sani dataset [article]

Javad Hassannataj Joloudari, Faezeh Azizi, Mohammad Ali Nematollahi, Roohallah Alizadehsani, Edris Hassannataj, Amir Mosavi
2021 arXiv   pre-print
Coronary heart disease (CAD) is one of the crucial reasons for cardiovascular mortality in middle-aged people worldwide. The most typical tool is angiography for diagnosing CAD.  ...  This proposed method has the highest accuracy of 89.45% through a 10-fold cross-validation technique with 35 selected features on the Z-Alizadeh Sani dataset.  ...  Such an algorithm is a particular type of evolutionary algorithm that uses evolutionary biology techniques such as inheritance and mutation.  ... 
arXiv:2108.08292v1 fatcat:q2c7fky7fzhdljve5bs43b7guu

A Survey of Evolutionary Algorithms for Decision-Tree Induction

Rodrigo Coelho Barros, Márcio Porto Basgalupp, André C. P. L. F. de Carvalho, Alex A. Freitas
2012 IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)  
Finally, a number of references is provided that describe applications of evolutionary algorithms for decision tree induction in different domains.  ...  This paper presents a survey of evolutionary algorithms designed for decision tree induction.  ...  In [136] , the authors present a new outlier prediction system for improving the classification performance in medical data mining. Two cardiovascular data sets are investigated.  ... 
doi:10.1109/tsmcc.2011.2157494 fatcat:wo3ak7qiwrcz5fosspwjgstjfi

Feature Selection Using Artificial Gorilla Troop Optimization for Biomedical Data: A Case Analysis with COVID-19 Data

Jayashree Piri, Puspanjali Mohapatra, Biswaranjan Acharya, Farhad Soleimanian Gharehchopogh, Vassilis C. Gerogiannis, Andreas Kanavos, Stella Manika
2022 Mathematics  
), (3) bi-objective (filter wrapper hybrid) (MO-DAGTO2), and (4) tri-objective (filter wrapper hybrid) (MO-DAGTO3) for identifying relevant features in diagnosing a particular disease.  ...  Here, a novel discrete artificial gorilla troop optimization (DAGTO) technique is introduced for the first time to handle FS tasks in the healthcare sector.  ...  Few studies have attempted to integrate filter and wrapper models by using evolutionary computing (EC) techniques, as the most extant EC algorithms follow one of these two models: filter or wrapper.  ... 
doi:10.3390/math10152742 fatcat:ucaomkcsmvh7xm5fio544i55ba
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