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.

scholarly journals Coronary Heart Disease Interpretation Based on Deep Neural Network

2019 ◽  
Vol 8 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Annisa Darmawahyuni ◽  
Siti Nurmaini ◽  
Firdaus Firdaus
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Predictive Accuracy ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Binary Classification ◽  
Confusion Matrix ◽  
Diagnostic Methods

Coronary heart disease (CHD) population increases every year with a significant number of deaths. Moreover, the mortality from coronary heart disease gets the highest prevalence in Indonesia at 1.5 percent. The misdiagnosis of coronary heart disease is a crucial fundamental that is the major factor that caused death. To prevent misdiagnosis of CHD, an intelligent system has been designed. This paper proposed a simulation which can be used to diagnose the coronary heart disease in better performance than the traditional diagnostic methods. Some researches have developed a system using conventional neural network or other machine learning algorithm, but the results are not a good performance. Based on a conventional neural network, deeper neural network (DNN) is proposed to our model in this work. As known as, the neural network is a supervised learning algorithm that good in the classification task. In DNN model, the implementation of binary classification was implemented to diagnose CHD present (representative “1”) or CHD absent (representative “0”). To help performance analysis using the UCI machine learning repository heart disease dataset, ROC Curve and its confusion matrix were implemented in this work. The overall predictive accuracy, sensitivity, and specificity acquired was 96%, 99%, 92%, respectively.

scholarly journals Prediction of functional outcomes of schizophrenia with genetic biomarkers using a bagging ensemble machine learning method with feature selection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eugene Lin ◽  
Chieh-Hsin Lin ◽  
Hsien-Yuan Lane
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Functional Outcome ◽  
Functional Outcomes ◽  
Learning Algorithm ◽  
Machine Learning Method ◽  
Ensemble Machine Learning ◽  
Predictive Algorithms ◽  
Ensemble Algorithm ◽  
Bagging Ensemble

AbstractGenetic variants such as single nucleotide polymorphisms (SNPs) have been suggested as potential molecular biomarkers to predict the functional outcome of psychiatric disorders. To assess the schizophrenia’ functional outcomes such as Quality of Life Scale (QLS) and the Global Assessment of Functioning (GAF), we leveraged a bagging ensemble machine learning method with a feature selection algorithm resulting from the analysis of 11 SNPs (AKT1 rs1130233, COMT rs4680, DISC1 rs821616, DRD3 rs6280, G72 rs1421292, G72 rs2391191, 5-HT2A rs6311, MET rs2237717, MET rs41735, MET rs42336, and TPH2 rs4570625) of 302 schizophrenia patients in the Taiwanese population. We compared our bagging ensemble machine learning algorithm with other state-of-the-art models such as linear regression, support vector machine, multilayer feedforward neural networks, and random forests. The analysis reported that the bagging ensemble algorithm with feature selection outperformed other predictive algorithms to forecast the QLS functional outcome of schizophrenia by using the G72 rs2391191 and MET rs2237717 SNPs. Furthermore, the bagging ensemble algorithm with feature selection surpassed other predictive algorithms to forecast the GAF functional outcome of schizophrenia by using the AKT1 rs1130233 SNP. The study suggests that the bagging ensemble machine learning algorithm with feature selection might present an applicable approach to provide software tools for forecasting the functional outcomes of schizophrenia using molecular biomarkers.

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.

scholarly journals Data mining techniques with machine learning algorithm to predict patients of heart disease

2021 ◽  
Vol 1088 (1) ◽  
pp. 012035
Author(s):  
Mulyawan ◽  
Agus Bahtiar ◽  
Githera Dwilestari ◽  
Fadhil Muhammad Basysyar ◽  
Nana Suarna
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Heart Disease ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Data Mining Techniques

scholarly journals A REVIEW OF FEATURE EXTRACTION METHODS ON MACHINE LEARNING

2021 ◽  
Vol 6 (22) ◽  
pp. 51-59
Author(s):  
Mustazzihim Suhaidi ◽  
Rabiah Abdul Kadir ◽  
Sabrina Tiun
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Feature Selection ◽  
Input Data ◽  
Feature Vector ◽  
Learning Algorithm ◽  
Extraction Methods ◽  
Machine Learning Algorithm ◽  
Learning Tasks ◽  
Low Dimensional

Extracting features from input data is vital for successful classification and machine learning tasks. Classification is the process of declaring an object into one of the predefined categories. Many different feature selection and feature extraction methods exist, and they are being widely used. Feature extraction, obviously, is a transformation of large input data into a low dimensional feature vector, which is an input to classification or a machine learning algorithm. The task of feature extraction has major challenges, which will be discussed in this paper. The challenge is to learn and extract knowledge from text datasets to make correct decisions. The objective of this paper is to give an overview of methods used in feature extraction for various applications, with a dataset containing a collection of texts taken from social media.

scholarly journals Recognition Technology of Athlete’s Limb Movement Combined Based on the Integrated Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Fei Tan ◽  
Xiaoqing Xie
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Learning Algorithm ◽  
Human Motion ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Recording Device ◽  
Table Tennis ◽  
Movement Recognition ◽  
Random Forest Tree

Human motion recognition based on inertial sensor is a new research direction in the field of pattern recognition. It carries out preprocessing, feature selection, and feature selection by placing inertial sensors on the surface of the human body. Finally, it mainly classifies and recognizes the extracted features of human action. There are many kinds of swing movements in table tennis. Accurately identifying these movement modes is of great significance for swing movement analysis. With the development of artificial intelligence technology, human movement recognition has made many breakthroughs in recent years, from machine learning to deep learning, from wearable sensors to visual sensors. However, there is not much work on movement recognition for table tennis, and the methods are still mainly integrated into the traditional field of machine learning. Therefore, this paper uses an acceleration sensor as a motion recording device for a table tennis disc and explores the three-axis acceleration data of four common swing motions. Traditional machine learning algorithms (decision tree, random forest tree, and support vector) are used to classify the swing motion, and a classification algorithm based on the idea of integration is designed. Experimental results show that the ensemble learning algorithm developed in this paper is better than the traditional machine learning algorithm, and the average recognition accuracy is 91%.

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