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30.05.2024 | Research

Detection of Cardiovascular Diseases Using Data Mining Approaches: Application of an Ensemble-Based Model

verfasst von: Mojdeh Nazari, Hassan Emami, Reza Rabiei, Azamossadat Hosseini, Shahabedin Rahmatizadeh

Erschienen in: Cognitive Computation | Ausgabe 5/2024

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Abstract

Cardiovascular diseases are the leading contributor of mortality worldwide. Accurate cardiovascular disease prediction is crucial, and the application of machine learning and data mining techniques could facilitate decision-making and improve predictive capabilities. This study aimed to present a model for accurate prediction of cardiovascular diseases and identifying key contributing factors with the greatest impact. The Cleveland dataset besides the locally collected dataset, called the Noor dataset, was used in this study. Accordingly, various data mining techniques besides four ensemble learning-based models were implemented on both datasets. Moreover, a novel model for combining individual classifiers in ensemble learning, wherein weights were assigned to each classifier (using a genetic algorithm), was developed. The predictive strength of each feature was also investigated to ensure the generalizability of the outcomes. The ultimate ensemble-based model achieved a precision rate of 88.05% and 90.12% on the Cleveland and Noor datasets, respectively, demonstrating its reliability and suitability for future research in predicting the likelihood of cardiovascular diseases. Not only the proposed model introduces an innovative approach for specifying cardiovascular diseases by unraveling the intricate relationships between various biological variables but also facilitates early detection of cardiovascular diseases.

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Metadaten
Titel
Detection of Cardiovascular Diseases Using Data Mining Approaches: Application of an Ensemble-Based Model
verfasst von
Mojdeh Nazari
Hassan Emami
Reza Rabiei
Azamossadat Hosseini
Shahabedin Rahmatizadeh
Publikationsdatum
30.05.2024
Verlag
Springer US
Erschienen in
Cognitive Computation / Ausgabe 5/2024
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-024-10306-z

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