Ensemble Methods for Heart Disease Prediction

Autor:	Talha Karadeniz, Gul Tokdemir, Hadi Hakan Maras
Rok vydání:	2021
Předmět:	Heart disease Computer Networks and Communications Computer science business.industry Estimator 02 engineering and technology Machine learning computer.software_genre medicine.disease Ensemble learning Theoretical Computer Science Lasso (statistics) Hardware and Architecture 020204 information systems Classifier (linguistics) 0202 electrical engineering electronic engineering information engineering medicine Graph (abstract data type) Classification methods 020201 artificial intelligence & image processing Artificial intelligence business computer Software Randomness
Zdroj:	New Generation Computing. 39:569-581
ISSN:	1882-7055 0288-3635
DOI:	10.1007/s00354-021-00124-4
Popis:	Heart disease prediction is a critical task regarding human health. It is based on deriving an Machine Learning model from medical parameters to predict risk levels. In this work, we propose and test novel ensemble methods for heart disease prediction. Randomness analysis of distance sequences is utilized to derive a classifier, which is served as a base estimator of a bagging scheme. Method is successfully tested on medical Spectf dataset. Additionally, a Graph Lasso and Ledoit–Wolf shrinkage-based classifier is developed for Statlog dataset which is a UCI data. These two algorithms yield comparatively good accuracy results: 88.7 and 88.8 for Spectf and Statlog, respectively. These proposed algorithms provide promising results and novel classification methods that can be utilized in various domains to improve performance of ensemble methods.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::277dabc5038d9e6a6a41c8108edd8382 https://doi.org/10.1007/s00354-021-00124-4 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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