Original scientific paper
https://doi.org/10.32985/ijeces.14.7.7
A Robust Cardiovascular Disease Predictor Based on Genetic Feature Selection and Ensemble Learning Classification
P.A Sadiyamole
S Manju Priya
; Professor, Department of CS, Karpagam Academy of Higher Education Coimbatore 21, India
Abstract
Timely detection of heart diseases is crucial for treating cardiac patients prior to the occurrence of any fatality. Automated early detection of these diseases is a necessity in areas where specialized doctors are limited. Deep learning methods provided with a decent set of heart disease data can be used to achieve this. This article proposes a robust heart disease prediction strategy using genetic algorithms and ensemble deep learning techniques. The efficiency of genetic algorithms is utilized to select more significant features from a high-dimensional dataset, combined with deep learning techniques such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Multi-Layer Perceptron (MLP), and Radial Basis Function (RBF), to achieve the goal. The boosting algorithm, Logit Boost, is made use of as a meta-learning classifier for predicting heart disease. The Cleveland heart disease dataset found in the UCI repository yields an overall accuracy of 99.66%, which is higher than many of the most efficient approaches now in existence.
Keywords
Cardiovascular disease prediction; Deep learning techniques; Genetic algorithms; Adaptive Neuro-Fuzzy Inference System; Multi-Layer Perceptron; Radial Basis Function; Logit Boost;
Hrčak ID:
307909
URI
Publication date:
11.9.2023.
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