Izvorni znanstveni članak
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
* Dopisni autor.
Sažetak
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.
Ključne riječi
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
Datum izdavanja:
11.9.2023.
Posjeta: 488 *