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A hybrid machine learning approach to identify coronary diseases using feature selection mechanism on heart disease dataset

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Publicado en:Distributed and Parallel Databases vol. 41, no. 1 (Jun 2023), p. 1
Autor principal: Doppala, Bhanu Prakash
Otros Autores: Bhattacharyya, Debnath, Chakkravarthy, Midhun, Kim, Tai-hoon
Publicado:
Springer Nature B.V.
Materias:
India
Machine learning
Asian people
Accuracy
Fatalities
Datasets
Regression analysis
Genetic algorithms
Radial basis function
Artificial intelligence
Cardiovascular disease
Researchers
Feature selection
Illnesses
Algorithms
Heart
Heart diseases
Acceso en línea:Citation/Abstract
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Resumen:Coronary illness can be treated as one of the major causes for mortality globally. On-time and Precise conclusion on the type of disease is significant for therapy and breakdown expectancy. Research scientists are working rigorously in their respective fields to reduce the death rate. Even though lot of research took place on this area still there is a scope for increasing the prediction accuracy. The fundamental point of our proposed work is to build up a hybrid methodology using genetic algorithm (GA) with (RBF) radial basis function (GA-RBF) for the detection of coronary sickness with increased accuracy using the feature selection mechanism. The proposed system performance achieved an accuracy of 85.40% using 14 attributes, and the prediction accuracy increased to 94.20% with nine characteristics where the functionality of the proposed system performed much better after attribute reduction.
ISSN:0926-8782
1573-7578
DOI:10.1007/s10619-021-07329-y
Fuente:Advanced Technologies & Aerospace Database