Reference Hub

This research has been cited in:

Article
Performance evaluation of Machine Learning models to predict heart attackMachine Graphics and Vision10.22630/MGV.2023.32.1.6
Chapter
Detection of Hepatitis B Virus Using NFC-Enabled Smartphone-Based Portable Amperometric ImmunosensorICT Infrastructure and Computing10.1007/978-981-19-5331-6_16
Conference
Detection of lung disease using novel deep convolutional neural network in comparison with genetic algorithm with improved accuracyINTERNATIONAL CONFERENCE ON SCIENCE, ENGINEERING, AND TECHNOLOGY 2022: Conference Proceedings10.1063/5.0179814
Conference
Prediction of breast cancer using Find-S and Candidate elimination algorithm2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS)10.1109/CSITSS47250.2019.9031046
Conference
Prediction of Heart Disease using Random Forest in Comparison with Logistic Regression to Measure Accuracy2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)10.1109/ACCAI58221.2023.10199851
Article
Heart disease prediction using distinct artificial intelligence techniques: performance analysis and comparisonIran Journal of Computer Science10.1007/s42044-023-00148-7
Chapter
A Novel Approach for Heart Disease Prediction Using Genetic Algorithm and Ensemble ClassificationIntelligent Systems and Applications10.1007/978-3-030-55187-2_36
Conference
Computational Techniques for Cardiovascular Diseases Prediction: Systematic Review2023 International Conference on Science, Engineering and Business for Sustainable Development Goals (SEB-SDG)10.1109/SEB-SDG57117.2023.10124568
Article
Health Care Data Analytics – Comparative Study of Supervised ModelInternational Journal of Innovative Technology and Exploring Engineering10.35940/ijitee.F9906.0511622
Article
A twin logistic regression method based on attribute-oriented fuzzy rough setJournal of Intelligent & Fuzzy Systems10.3233/JIFS-222986
Article
Building prediction models and discovering important factors of health insurance fraud using machine learning methodsJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-023-04633-6
Article
Predictive Systems: Role of Feature Selection in Prediction of Heart DiseaseJournal of Physics: Conference Series10.1088/1742-6596/1372/1/012074
Article
Applied Research on Heart Disease Prediction Based on Improved DeepFMComputer Science and Application10.12677/CSA.2021.118217
Article
Hybrid MRK-Means + + RBM Model: An Efficient Heart Disease Predicting System Using ModifiedRoughK-Means + + Algorithm and Restricted Boltzmann MachineInternational Journal of Uncertainty, Fuzziness and Knowledge-Based Systems10.1142/S0218488523400056
Chapter
Big Data Analytics in HealthcareCognitive Intelligence and Big Data in Healthcare10.1002/9781119771982.ch10
Article
An Efficient Filter and Wrapper based Selection Methods along With Random Forest and Support Vector Machines Classification Technique in Health Care SystemJournal of Machine and Computing10.53759/7669/jmc202303048
Chapter
Prediction of Heart Disease Using Genetic AlgorithmProceedings of Second Doctoral Symposium on Computational Intelligence10.1007/978-981-16-3346-1_4
Chapter
Building an Efficient Heart Disease Prediction System by using Clustering TechniquesHigh Performance Computing and Networking10.1007/978-981-16-9885-9_6
Article
Predicting Equivalent Water Thickness in Wheat Using UAV Mounted Multispectral Sensor through Deep Learning TechniquesRemote Sensing10.3390/rs13214476
Article
Efficient prediction of coronary artery disease using machine learning algorithms with feature selection techniquesComputers and Electrical Engineering10.1016/j.compeleceng.2024.109130
Chapter
Automatic Identification of Traditional Colombian Music Genres Based on Audio Content Analysis and Machine Learning TechniquesProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications10.1007/978-3-030-33904-3_61
Conference
Myocardial blood flow quantification for evaluation of coronary artery disease2021 International Conference on Forensics, Analytics, Big Data, Security (FABS)10.1109/FABS52071.2021.9702574
Article
A Data Mining Inspired Methodology towards the Identification of Diabetic RetinopathyIOP Conference Series: Materials Science and Engineering10.1088/1757-899X/1022/1/012082
Conference
Health Causal Probability Knowledge Graph2020 7th International Conference on Bioinformatics Research and Applications10.1145/3440067.3440077
Article
Learning Disease Causality Knowledge From the Web of Health DataInternational Journal on Semantic Web and Information Systems10.4018/IJSWIS.297145

Prediction of Heart Disease Using Random Forest and Rough Set Based Feature Selection

Indu Yekkala, Sunanda Dixit

Source Title: International Journal of Big Data and Analytics in Healthcare (IJBDAH)3(1)

ISSN: 2379-738X|EISSN: 2379-7371|EISBN13: 9781522547174|DOI: 10.4018/IJBDAH.2018010101

Cite Article Cite Article

MLA

Yekkala, Indu, and Sunanda Dixit. "Prediction of Heart Disease Using Random Forest and Rough Set Based Feature Selection." IJBDAH vol.3, no.1 2018: pp.1-12. http://doi.org/10.4018/IJBDAH.2018010101

APA

Yekkala, I. & Dixit, S. (2018). Prediction of Heart Disease Using Random Forest and Rough Set Based Feature Selection. International Journal of Big Data and Analytics in Healthcare (IJBDAH), 3(1), 1-12. http://doi.org/10.4018/IJBDAH.2018010101

Chicago

Yekkala, Indu, and Sunanda Dixit. "Prediction of Heart Disease Using Random Forest and Rough Set Based Feature Selection," International Journal of Big Data and Analytics in Healthcare (IJBDAH) 3, no.1: 1-12. http://doi.org/10.4018/IJBDAH.2018010101

Export Reference

Favorite Full-Issue Download

View Full Text HTML

View Full Text PDF

Abstract

Data is generated by the medical industry. Often this data is of very complex nature—electronic records, handwritten scripts, etc.—since it is generated from multiple sources. Due to the Complexity and sheer volume of this data necessitates techniques that can extract insight from this data in a quick and efficient way. These insights not only diagnose the diseases but also predict and can prevent disease. One such use of these techniques is cardiovascular diseases. Heart disease or coronary artery disease (CAD) is one of the major causes of death all over the world. Comprehensive research using single data mining techniques have not resulted in an acceptable accuracy. Further research is being carried out on the effectiveness of hybridizing more than one technique for increasing accuracy in the diagnosis of heart disease. In this article, the authors worked on heart stalog dataset collected from the UCI repository, used the Random Forest algorithm and Feature Selection using rough sets to accurately predict the occurrence of heart disease

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.

Username or email: *

Password: *

Forgot individual login password?

Create individual account

Prediction of Heart Disease Using Random Forest and Rough Set Based Feature Selection

MLA

APA

Chicago

Export Reference

Abstract

Request Access