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Multi-Objective Approach for Support Vector Machine Parameter Optimization and Variable Selection in Cardiovascular Predictive Modeling

Christina Brester, Ivan Ryzhikov, Tomi-Pekka Tuomainen, Ari Voutilainen, Eugene Semenkin, Mikko Kolehmainen
2018 Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics  
Multi-Objective Approach for Support Vector Machine Parameter Optimization and Variable Selection in Cardiovascular Predictive Modeling.  ...  We present a heuristic-based approach for Support Vector Machine (SVM) parameter optimization and variable selection using a real-valued cooperative Multi-Objective Evolutionary Algorithm (MOEA).  ...  In this paper, we develop a predictive system based on a Support Vector Machine (SVM) and a Multi-Objective Evolutionary Algorithm (MOEA), which is applied to tune SVM meta and kernel function parameters  ... 
doi:10.5220/0006866002090215 dblp:conf/icinco/BresterRTVSK18 fatcat:whkjttze35ciffxgwk7nvos53u

A Review of Clustering Technique Based on Different Optimization Function Using for Selection of Center Point

Kavita Firke
2017 International Journal for Research in Applied Science and Engineering Technology  
In this paper present the review of clustering technique for automatic validation and cluster center selection.  ...  used different optimization function such as genetic algorithm, particle swarm optimization algorithm and many more algorithm used for center selection process.  ...  Particle of swarm optimization controls the dynamic feature evaluation process and decreases the possibility of confusion in selection of class and increase the classification ratio of support vector machine  ... 
doi:10.22214/ijraset.2017.3008 fatcat:3sjoz5ti3bg5voyxusyr5n22wu

Personalized Clinical Treatment Selection Using Genetic Algorithm and Analytic Hierarchy Process

Olena Nosovets, Vitalii Babenko, Ilya Davydovych, Olena Petrunina, Olga Averianova, Le Dai Zyonh
2021 Advances in Science, Technology and Engineering Systems  
One of the most exciting challenges in this field is the automation of clinical treatment selection for patient state optimization.  ...  The development of Machine Learning methods and approaches offers enormous growth opportunities in the Healthcare field.  ...  Acknowledgment The authors would like to express their gratitude to the experts from Amosov National Institute of Cardiovascular Surgery who provided clinical data and assist with the research.  ... 
doi:10.25046/aj060446 fatcat:j4stkwnornderojfzmwrpiocte

Reducing Opioid Prescriptions by Identifying Responders on Topical Analgesic Treatment Using an Individualized Medicine and Predictive Analytics Approach

Jeffrey Gudin, Seferina Mavroudi, Aigli Korfiati, Konstantinos Theofilatos, Derek Dietze, Peter Hurwitz
2020 Journal of Pain Research  
A novel machine learning approach combining multi-objective optimization and support vector regression was used to build prediction models which can predict, using responses in the baseline, the four different  ...  support system for the selection of the treatment of chronic pain patients.  ...  Office by InSyBio Ltd which includes the description of the computational framework for predictive biomarkers and building predictive models for diagnosis, prognosis and treatment. D.  ... 
doi:10.2147/jpr.s246503 pmid:32547186 pmcid:PMC7266406 fatcat:ymnvqzkjnfgdhj2dqmfnjefkpe

Cardiovascular Disease Prediction Model using Machine Learning Algorithms

Siddhika Arunachalam
2020 International Journal for Research in Applied Science and Engineering Technology  
Machine Learning can play an important role in predicting cardiovascular disease and such information, if predicted well in advance can provide significant insights to doctors who can then adapt their  ...  This is accomplished by applying rules to the individual results of classification algorithms such as Gradient Boosting Classifier, Random Forest Classifier, Support Vector Machine, Extremely Randomized  ...  Fig. 4 Random forest tree 3) Support Vector Machine (SVM): Support Vector Machine is a machine learning algorithm which is used for both classification and regression tasks.  ... 
doi:10.22214/ijraset.2020.6164 fatcat:7l765lmrefawdp2gli3c3hdvzy

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  
In this paper we compare a number of two-criterion filtering techniques for feature selection in cardiovascular predictive modelling.  ...  To find attribute subsystems meeting the introduced criteria in an optimal way, we suggest applying a cooperative multi-objective genetic algorithm.  ...  Support Vector Machine is applied as a predictive model.  ... 
doi:10.5220/0005971101400145 dblp:conf/icinco/BresterKTSK16 fatcat:5bzmrrhnxbbcljiudepwwgriea

Comparison of machine learning methods for the classification of cardiovascular disease

Rachael Hagan, Charles J. Gillan, Fiona Mallett
2021 Informatics in Medicine Unlocked  
The authors are grateful to the UCI Repository of Machine Learning Databases [11] and the owners of the Kaggle site where we obtained the data sets used in this work.  ...  Acknowledgment RH acknowledges funding for a PhD studentship from the Department for the Economy Northern Ireland.  ...  Support vector machine Following equations (1) and (2) , in a support vector machine model [24] approach to build the function f in equation (2) , we take each observation vector, x i , and augment  ... 
doi:10.1016/j.imu.2021.100606 fatcat:p4xhalvkuvbsjof5nmm6dm25uq

The Severity Prediction of The Binary And Multi-Class Cardiovascular Disease – A Machine Learning-Based Fusion Approach [article]

Hafsa Binte Kibria, Abdul Matin
2022 arXiv   pre-print
The proposed approach has been experimented with different test training ratios for binary and multiclass classification problems, and for both of them, the fusion models performed well.  ...  By using the machine learning technique, we can look for hidden data patterns to predict various diseases.  ...  Support vector machine, random forest, decision tree, linear and logistic regression were used for prediction.  ... 
arXiv:2203.04921v1 fatcat:ae6zotbr2vcovp5o3t6k6vxrde

Evolutionary Machine Learning: A Survey

Akbar Telikani, Amirhessam Tahmassebi, Wolfgang Banzhaf, Amir H. Gandomi
2022 ACM Computing Surveys  
), and postprocessing (e.g., rule optimization, decision tree/support vectors pruning, and ensemble learning).  ...  Evolutionary approaches can be used in all three parts of ML: preprocessing (e.g., feature selection and resampling), learning (e.g., parameter setting, membership functions, and neural network topology  ...  Evolutionary Support Vector Machine. The idea of support vector machines is based on an optimally separating hyper-plane.  ... 
doi:10.1145/3467477 fatcat:o6m3nekqfnaudjnxxoeferhine

Multi-Label Active Learning-Based Machine Learning Model for Heart Disease Prediction

Ibrahim M. El-Hasnony, Omar M. Elzeki, Ali Alshehri, Hanaa Salem
2022 Sensors  
In this paper, five (MMC, Random, Adaptive, QUIRE, and AUDI) selection strategies for multi-label active learning were applied and used for reducing labelling costs by iteratively selecting the most relevant  ...  The selection methods with a label ranking classifier have hyperparameters optimized by a grid search to implement predictive modelling in each scenario for the heart disease dataset.  ...  Many algorithms for predictive learning are available (e.g., linear and logistic regression, classification and regression trees, learning vector quantization (LVQ), support vector machines (SVM), boosting  ... 
doi:10.3390/s22031184 pmid:35161928 pmcid:PMC8839067 fatcat:ckt4naiphfh4fpyy5a7prbg4ry

Physician-Friendly Machine Learning: A Case Study with Cardiovascular Disease Risk Prediction

Meghana Padmanabhan, Pengyu Yuan, Govind Chada, Hien Van Nguyen
2019 Journal of Clinical Medicine  
In particular, the graduate student manually builds multiple machine learning classifiers and tunes their parameters for one month using scikit-learn library, which is a popular machine learning library  ...  We study the case of predicting the risk of cardiovascular diseases.  ...  Abbreviations The following abbreviations are used in this manuscript:  ... 
doi:10.3390/jcm8071050 pmid:31323843 pmcid:PMC6678298 fatcat:bpapqfz7ejalnih7a4my6uvhja

Body fat predicts exercise capacity in persons with Type 2 Diabetes Mellitus: A machine learning approach

Tanmay Nath, Rexford S. Ahima, Prasanna Santhanam, Sabine Rohrmann
2021 PLoS ONE  
Thereafter, we used different machine learning methods to predict maximum exercise capacity. The different machine learning models showed a strong predictive performance for both females and males.  ...  We show that subtotal fat percentage is the most important feature for predicting the exercise capacity for males and females after accounting for other important variables.  ...  Acknowledgments The authors wish to thank the staff and participants of the Look AHEAD Study for their valuable contributions.  ... 
doi:10.1371/journal.pone.0248039 pmid:33788855 fatcat:akf5oz5wevawtmqera5qfixxpe

Discovery of novel therapeutic properties of drugs from transcriptional responses based on multi-label classification

Lingwei Xie, Song He, Yuqi Wen, Xiaochen Bo, Zhongnan Zhang
2017 Scientific Reports  
In this paper, we developed a systematic method to discover novel indications for existing drugs by approaching drug repositioning as a multi-label classification task and used a Softmax regression model  ...  Second, machine learning algorithms have been applied successfully in biomedical research.  ...  Specifically, the number of parameters or parameter vectors should be similar between the "true" model and model we choose.  ... 
doi:10.1038/s41598-017-07705-8 pmid:28769090 pmcid:PMC5541064 fatcat:3vvbkquafrcuzho2xrlr45yfwa

Machine Learning: Assisted Cardiovascular Diseases Diagnosis

Aseel Alfaidi, Reem Aljuhani, Bushra Alshehri, Hajer Alwadei, Sahar Sabbeh
2022 International Journal of Advanced Computer Science and Applications  
This paper proposes a model for diagnosing the probability of an individual having cardiovascular illness by employing Machine Learning (ML) models.  ...  The experiments were executed using seven algorithms, and a public dataset of cardiovascular disease was used to train the models.  ...  , decision tree, random forest (RF), naïve Bayesian (NB), knearest neighbor (KNN), support vector machine (SVM), and multi-layer perceptron (MLP).  ... 
doi:10.14569/ijacsa.2022.0130216 fatcat:o3lrtbx2sfeevaoefv2d5e2isy

Grid Search for Predicting Coronary Heart Disease by Tuning Hyper-Parameters

S. Prabu, B. Thiyaneswaran, M. Sujatha, C. Nalini, Sujatha Rajkumar
2022 Computer systems science and engineering  
Three models with a cross-validation approach do the required task. Feature Selection based on the use of statistical and correlation matrices for multivariate analysis.  ...  The study effort focuses on optimizing function selection, tweaking hyperparameters to improve model accuracy and the prediction of heart disease by examining Framingham datasets using random forestry  ...  Acknowledgement: The authors would like to thank Anna University and also we like to thank anonymous reviewers for their so-called insights.  ... 
doi:10.32604/csse.2022.022739 fatcat:faq6iscojngvxgoxnbxuvmiqjy
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