Heart Disease Detection Using Machine Learning Majority Voting Ensemble Method

2019 ◽  
Author(s):  
Rahma Atallah ◽  
Amjed Al-Mousa
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Majority Voting ◽  
Ensemble Method ◽  
Disease Detection

A Review on Heart Disease Detection Techniques

2017 ◽  
Vol 7 (7) ◽  
pp. 395
Author(s):  
. Anika ◽  
Navpreet Kaur
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Review Paper ◽  
Vascular Diseases ◽  
Disease Detection ◽  
Automated Classification ◽  
Feature Extraction Method ◽  
Detection Techniques ◽  
Cardio Vascular ◽  
Public Domain Software

The paper exhibits a formal audit on early detection of heart disease which are the major cause of death. Computational science has potential to detect disease in prior stages automatically. With this review paper we describe machine learning for disease detection. Machine learning is a method of data analysis that automates analytical model building.Various techniques develop to predict cardiac disease based on cases through MRI was developed. Automated classification using machine learning. Feature extraction method using Cell Profiler and GLCM. Cell Profiler a public domain software, freely available is flourished by the Broad Institute's Imaging Platform and Glcm is a statistical method of examining texture .Various techniques to detect cardio vascular diseases.

Heart Disease Detection using Machine Learning Technique

2021 ◽  
Author(s):  
Likitha KN ◽  
Nethravathi R ◽  
Nithyashree K ◽  
Ritika Kumari ◽  
Sridhar N ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Disease Detection ◽  
Machine Learning Technique ◽  
Learning Technique

scholarly journals Analysis of Heart Disease Using Parallel and Sequential Ensemble Methods With Feature Selection Techniques

2021 ◽  
Vol 6 (1) ◽  
pp. 40-56
Author(s):  
Dhyan Chandra Yadav ◽  
Saurabh Pal
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Random Forest ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Ensemble Methods ◽  
Machine Learning Algorithms ◽  
Ensemble Method ◽  
Gradient Boosting ◽  
High Classification Accuracy

This paper has organized a heart disease-related dataset from UCI repository. The organized dataset describes variables correlations with class-level target variables. This experiment has analyzed the variables by different machine learning algorithms. The authors have considered prediction-based previous work and finds some machine learning algorithms did not properly work or do not cover 100% classification accuracy with overfitting, underfitting, noisy data, residual errors on base level decision tree. This research has used Pearson correlation and chi-square features selection-based algorithms for heart disease attributes correlation strength. The main objective of this research to achieved highest classification accuracy with fewer errors. So, the authors have used parallel and sequential ensemble methods to reduce above drawback in prediction. The parallel and serial ensemble methods were organized by J48 algorithm, reduced error pruning, and decision stump algorithm decision tree-based algorithms. This paper has used random forest ensemble method for parallel randomly selection in prediction and various sequential ensemble methods such as AdaBoost, Gradient Boosting, and XGBoost Meta classifiers. In this paper, the experiment divides into two parts: The first part deals with J48, reduced error pruning and decision stump and generated a random forest ensemble method. This parallel ensemble method calculated high classification accuracy 100% with low error. The second part of the experiment deals with J48, reduced error pruning, and decision stump with three sequential ensemble methods, namely AdaBoostM1, XG Boost, and Gradient Boosting. The XG Boost ensemble method calculated better results or high classification accuracy and low error compare to AdaBoostM1 and Gradient Boosting ensemble methods. The XG Boost ensemble method calculated 98.05% classification accuracy, but random forest ensemble method calculated high classification accuracy 100% with low error.

APPLICATION OF ENSEMBLE ALGORITHM INTEGRATING MULTIPLE CRITERIA FEATURE SELECTION IN CORONARY HEART DISEASE DETECTION

2017 ◽  
Vol 29 (06) ◽  
pp. 1750043 ◽  
Author(s):  
Cai-Jie Qin ◽  
Qiang Guan ◽  
Xin-Pei Wang
Keyword(s):  
Machine Learning ◽  
Coronary Heart Disease ◽  
Feature Selection ◽  
Heart Disease ◽  
Subjective Experience ◽  
Learning Algorithm ◽  
Low Cost ◽  
Detection Methods ◽  
Disease Detection ◽  
Ensemble Algorithm

Conventional coronary heart disease (CHD) detection methods are expensive, rely much on doctors’ subjective experience, and some of them have side effects. In order to obtain rapid, high-precision, low-cost, non-invasive detection results, several methods in machine learning were attempted for CHD detection in this paper. The paper adopted multiple evaluation criteria to measure features, combined with heuristic search strategy and seven common classification algorithms to verify the validity and the importance of feature selection (FS) in the Z-Alizadeh Sani CHD dataset. On this basis, a novelty algorithm integrating multiple FS methods into the ensemble algorithm (ensemble algorithm based on multiple feature selection, EA-MFS) was further proposed. The algorithm adopted Bagging approach to increase data diversity, used the aforementioned MFS methods for functional perturbation, employed major voting method to carry out the decision results, and performed selective integration in terms of the difference of base classifiers in the ensemble process. Compared with the single FS method, the EA-MFS algorithm could comprehensively describe the relationship of features, enhance the classification effect, and displayed better robustness. That meant the EA-MFS algorithm could reduce the dependence on dataset and strengthen the stability of the algorithm, all of which were of great significance for the clinical application of machine learning algorithm in coronary heart disease detection.

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