A Multicriteria Weighted Vote-Based Classifier Ensemble for Heart Disease Prediction

2015 ◽  
Vol 32 (4) ◽  
pp. 615-645 ◽  
Author(s):  
Saba Bashir ◽  
Usman Qamar ◽  
Farhan Hassan Khan
Keyword(s):  
Heart Disease ◽  
Classifier Ensemble ◽  
Disease Prediction ◽  
Weighted Vote

scholarly journals Heart disease prediction using data mining

2021 ◽  
Vol 1913 (1) ◽  
pp. 012099
Author(s):  
R Fadnavis ◽  
K Dhore ◽  
D Gupta ◽  
J Waghmare ◽  
D Kosankar
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Disease Prediction ◽  
Using Data

An intelligent heart disease prediction system based on swarm-artificial neural network

2021 ◽  
Author(s):  
Sudarshan Nandy ◽  
Mainak Adhikari ◽  
Venki Balasubramanian ◽  
Varun G. Menon ◽  
Xingwang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heart Disease ◽  
Disease Prediction ◽  
Prediction System ◽  
Artificial Neural

Ensemble classification technique for heart disease prediction with meta-heuristic-enabled training system

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Parvathaneni Rajendra Kumar ◽  
Suban Ravichandran ◽  
Satyala Narayana
Keyword(s):  
Heart Disease ◽  
Dimensionality Reduction ◽  
Research Work ◽  
Feature Reduction ◽  
Ensemble Classification ◽  
Support Vector ◽  
Disease Prediction ◽  
Central Tendency ◽  
Sea Lion ◽  
Qualitative Variation

AbstractObjectivesThis research work exclusively aims to develop a novel heart disease prediction framework including three major phases, namely proposed feature extraction, dimensionality reduction, and proposed ensemble-based classification.MethodsAs the novelty, the training of NN is carried out by a new enhanced optimization algorithm referred to as Sea Lion with Canberra Distance (S-CDF) via tuning the optimal weights. The improved S-CDF algorithm is the extended version of the existing “Sea Lion Optimization (SLnO)”. Initially, the statistical and higher-order statistical features are extracted including central tendency, degree of dispersion, and qualitative variation, respectively. However, in this scenario, the “curse of dimensionality” seems to be the greatest issue, such that there is a necessity of dimensionality reduction in the extracted features. Hence, the principal component analysis (PCA)-based feature reduction approach is deployed here. Finally, the dimensional concentrated features are fed as the input to the proposed ensemble technique with “Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN)” with optimized Neural Network (NN) as the final classifier.ResultsAn elaborative analyses as well as discussion have been provided by concerning the parameters, like evaluation metrics, year of publication, accuracy, implementation tool, and utilized datasets obtained by various techniques.ConclusionsFrom the experiment outcomes, it is proved that the accuracy of the proposed work with the proposed feature set is 5, 42.85, and 10% superior to the performance with other feature sets like central tendency + dispersion feature, central tendency qualitative variation, and dispersion qualitative variation, respectively.ResultsFinally, the comparative evaluation shows that the presented work is appropriate for heart disease prediction as it has high accuracy than the traditional works.

scholarly journals Machine Learning Based Classification Models for Heart Disease Prediction

2021 ◽  
Vol 1916 (1) ◽  
pp. 012092
Author(s):  
N Karthikeyan ◽  
P Padmanaban ◽  
A Prasanth ◽  
D Ragunath
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Disease Prediction ◽  
Classification Models
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