scholarly journals A hybrid cost-sensitive ensemble for heart disease prediction

2020 ◽  
Author(s):  
Zhenya Qi ◽  
Zuoru Zhang
Keyword(s):  
Heart Disease ◽  
Statistical Tests ◽  
Disease Diagnosis ◽  
Medical Decision ◽  
Support Vector ◽  
Ensemble Model ◽  
Misclassification Cost ◽  
Promising Alternative ◽  
Relief Algorithm ◽  
Proposed Model

Abstract Background: Heart disease is the primary cause of morbidity and mortality in the world. It includes numerous problems and symptoms. The diagnosis of heart disease is difficult because there are too many factors to analyze. What’s more, the misclassification cost could be very high. Methods: A cost-sensitive ensemble model was proposed to improve the efficiency of diagnosis and reduce the misclassification cost. The proposed model contains five heterogeneous classifiers: random forest, logistic regression, support vector machine, extreme learning machine and k-nearest neighbor. T-test was used to investigate if the performance of the ensemble model was better than individual classifiers and the contribution of Relief algorithm. Results: The best performance was achieved by the proposed model according to ten-fold cross validation. The statistical tests demonstrated that the performance of the proposed model was significantly superior to individual classifiers, and the efficiency of classification was distinctively improved by Relief algorithm. Conclusions: The proposed ensemble model gained significantly better results compared with individual classifiers and previous studies, which implies that it can be used as a promising alternative tool in medical decision making for heart disease diagnosis.

scholarly journals A hybrid cost-sensitive ensemble for heart disease prediction

2020 ◽  
Author(s):  
Zhenya Qi ◽  
Zuoru Zhang
Keyword(s):  
Heart Disease ◽  
Statistical Tests ◽  
Disease Diagnosis ◽  
Medical Decision ◽  
Support Vector ◽  
Ensemble Model ◽  
Misclassification Cost ◽  
Promising Alternative ◽  
Relief Algorithm ◽  
Proposed Model

Abstract Background: Heart disease is the primary cause of morbidity and mortality in the world. It includes numerous problems and symptoms. The diagnosis of heart disease is difficult because there are too many factors to analyze. What’s more, the misclassification cost could be very high. Methods: A cost-sensitive ensemble model was proposed to improve the efficiency of diagnosis and reduce the misclassification cost. The proposed model contains five heterogeneous classifiers: random forest, logistic regression, support vector machine, extreme learning machine and k-nearest neighbor. T-test was used to investigate if the performance of the ensemble model was better than individual classifiers and the contribution of Relief algorithm. Results: The best performance was achieved by the proposed model according to ten-fold cross validation. The statistical tests demonstrated that the performance of the proposed model was significantly superior to individual classifiers, and the efficiency of classification was distinctively improved by Relief algorithm. Conclusions: The proposed ensemble model gained significantly better results compared with individual classifiers and previous studies, which implies that it can be used as a promising alternative tool in medical decision making for heart disease diagnosis.

scholarly journals A hybrid cost-sensitive ensemble for heart disease prediction

2021 ◽  
Author(s):  
Zhenya Qi ◽  
Zuoru Zhang
Keyword(s):  
Heart Disease ◽  
Nearest Neighbor ◽  
Statistical Tests ◽  
Disease Diagnosis ◽  
Medical Decision ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Misclassification Cost ◽  
Promising Alternative ◽  
Relief Algorithm

Abstract Background: Heart disease is the primary cause of morbidity and mortality in the world. It includes numerous problems and symptoms. The diagnosis of heart disease is difficult because there are too many factors to analyze. What's more, the misclassification cost could be very high. Methods: A cost-sensitive ensemble method was proposed to improve the efficiency of diagnosis and reduce the misclassification cost. The proposed method contains five heterogeneous classifiers: random forest, logistic regression, support vector machine, extreme learning machine and k-nearest neighbor. T-test was used to investigate if the performance of the ensemble was better than individual classifiers and the contribution of Relief algorithm. Results: The best performance was achieved by the proposed method according to ten-fold cross validation. The statistical tests demonstrated that the performance of the proposed ensemble was significantly superior to individual classifiers, and the efficiency of classification was distinctively improved by Relief algorithm. Conclusions: The proposed ensemble gained significantly better results compared with individual classifiers and previous studies, which implies that it can be used as a promising alternative tool in medical decision making for heart disease diagnosis.

scholarly journals A hybrid cost-sensitive ensemble for heart disease prediction

2020 ◽  
Author(s):  
Zhenya Qi ◽  
Zuoru Zhang
Keyword(s):  
Heart Disease ◽  
Nearest Neighbor ◽  
Statistical Tests ◽  
Disease Diagnosis ◽  
Medical Decision ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Misclassification Cost ◽  
Promising Alternative ◽  
Relief Algorithm

Abstract Background: Heart disease is the primary cause of morbidity and mortality in the world. It includes numerous problems and symptoms. The diagnosis of heart disease is difficult because there are too many factors to analyze. What's more, the misclassification cost could be very high. Methods: A cost-sensitive ensemble method was proposed to improve the efficiency of diagnosis and reduce the misclassification cost. The proposed method contains five heterogeneous classifiers: random forest, logistic regression, support vector machine, extreme learning machine and k-nearest neighbor. T-test was used to investigate if the performance of the ensemble was better than individual classifiers and the contribution of Relief algorithm. Results: The best performance was achieved by the proposed method according to ten-fold cross validation. The statistical tests demonstrated that the performance of the proposed ensemble was significantly superior to individual classifiers, and the efficiency of classification was distinctively improved by Relief algorithm. Conclusions: The proposed ensemble gained significantly better results compared with individual classifiers and previous studies, which implies that it can be used as a promising alternative tool in medical decision making for heart disease diagnosis.

scholarly journals A hybrid cost-sensitive ensemble for heart disease prediction

2020 ◽  
Author(s):  
Zhenya Qi ◽  
Zuoru Zhang
Keyword(s):  
Heart Disease ◽  
Nearest Neighbor ◽  
Statistical Tests ◽  
Disease Diagnosis ◽  
Medical Decision ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Misclassification Cost ◽  
Promising Alternative ◽  
Relief Algorithm

Abstract Background: Heart disease is the primary cause of morbidity and mortality in the world. It includes numerous problems and symptoms. The diagnosis of heart disease is difficult because there are too many factors to analyze. What's more, the misclassification cost could be very high. Methods: A cost-sensitive ensemble method was proposed to improve the efficiency of diagnosis and reduce the misclassification cost. The proposed method contains five heterogeneous classifiers: random forest, logistic regression, support vector machine, extreme learning machine and k-nearest neighbor. T-test was used to investigate if the performance of the ensemble was better than individual classifiers and the contribution of Relief algorithm. Results: The best performance was achieved by the proposed method according to ten-fold cross validation. The statistical tests demonstrated that the performance of the proposed ensemble was significantly superior to individual classifiers, and the efficiency of classification was distinctively improved by Relief algorithm. Conclusions: The proposed ensemble gained significantly better results compared with individual classifiers and previous studies, which implies that it can be used as a promising alternative tool in medical decision making for heart disease diagnosis.

scholarly journals A hybrid cost-sensitive ensemble for heart disease prediction

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Qi Zhenya ◽  
Zuoru Zhang
Keyword(s):  
Heart Disease ◽  
Nearest Neighbor ◽  
Statistical Tests ◽  
Disease Diagnosis ◽  
Medical Decision ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Misclassification Cost ◽  
Promising Alternative ◽  
Relief Algorithm

Abstract Background Heart disease is the primary cause of morbidity and mortality in the world. It includes numerous problems and symptoms. The diagnosis of heart disease is difficult because there are too many factors to analyze. What’s more, the misclassification cost could be very high. Methods A cost-sensitive ensemble method was proposed to improve the efficiency of diagnosis and reduce the misclassification cost. The proposed method contains five heterogeneous classifiers: random forest, logistic regression, support vector machine, extreme learning machine and k-nearest neighbor. T-test was used to investigate if the performance of the ensemble was better than individual classifiers and the contribution of Relief algorithm. Results The best performance was achieved by the proposed method according to ten-fold cross validation. The statistical tests demonstrated that the performance of the proposed ensemble was significantly superior to individual classifiers, and the efficiency of classification was distinctively improved by Relief algorithm. Conclusions The proposed ensemble gained significantly better results compared with individual classifiers and previous studies, which implies that it can be used as a promising alternative tool in medical decision making for heart disease diagnosis.

scholarly journals A hybrid cost-sensitive ensemble for heart disease prediction

2020 ◽  
Author(s):  
Zhenya Qi ◽  
Zuoru Zhang
Keyword(s):  
Heart Disease ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Misclassification Cost ◽  
Proposed Model ◽  
Learning Machine ◽  
Fold Cross Validation ◽  
Very High

Abstract Heart disease is the primary cause of morbidity and mortality in the world. It includes numerous problems and symptoms. The diagnosis of heart disease is difficult because there are too many factors to analyze. What's more, the misclassification cost could be very high. In this paper, I firstly propose a cost-sensitive ensemble model to improve the accuracy of diagnosis and reduce the misclassification cost. The proposed model contains five heterogeneous classifiers: random forest, logistic regression, support vector machine, extreme learning machine and k-nearest neighbor. Then, experiments are done on three datasets from UCI machine learning repository. The highest classification accuracy of 91.74%, highest G-mean of 90.55%, highest precision of 96.11%, highest recall of 89.61% and lowest misclassification cost of 30.32% are achieved by the proposed model according to ten-fold cross validation. The results demonstrate that the performance of the proposed model is superior to those of previously reported classification techniques.

scholarly journals A Genetic Algorithm based Hybrid Deep Learning Approach for Heart Disease Prediction

2021 ◽  
Vol 16 (3) ◽  
Author(s):  
Khushbu Verma ◽  
Ankit Singh Bartwal ◽  
Mathura Prasad Thapliyal
Keyword(s):  
Genetic Algorithm ◽  
Heart Disease ◽  
Deep Learning ◽  
Early Stage ◽  
Support Vector ◽  
Disease Prediction ◽  
The Past ◽  
Lifestyle Choices ◽  
Proposed Model ◽  
Tree Algorithms

People nowadays suffer from a variety of heart ailments as a result of the environment and their lifestyle choices. As a result, analyzing sickness at an early stage becomes a critical responsibility. Data mining uses disease data to uncover important knowledge. In this research paper, we employ the hybrid combination of a Genetic Algorithm based Feature selection and Ensemble Deep Neural Network Model for Heart Disease prediction. In this algorithm, we used a 0.04 learning rate and Adam optimizer was used for enhancement of the proposed model. The proposed algorithm has come to 98% accuracy of heart disease prediction, which is higher than the past approaches. Other exist models such as random forest, logistic regression, support vector machine, Decision tree algorithms have taken a higher time and give less accuracy compare to the proposed hybrid deep learning-based approach.

Mobile Cancer Prophecy System to Assist Patients: Big Data Analysis and Design

2019 ◽  
Vol 16 (8) ◽  
pp. 3623-3628
Author(s):  
E. Suganya ◽  
S. Sountharrajan ◽  
Shishir K. Shandilya ◽  
M. Karthiga
Keyword(s):  
Big Data ◽  
Mobile Devices ◽  
Disease Diagnosis ◽  
Disease Classification ◽  
Support Vector ◽  
Cancer Disease ◽  
Analysis And Design ◽  
Proposed Model ◽  
Smart Mobile ◽  
Smart Mobile Devices

The growth of cancer in India is growing hastily in recent years. Efficient monitoring and medication procedures are needed in high demand. Recent research states diagnose of cancer during its early break through will prevent mortality. The evolution of smart mobile devices paves its mutual focus in healthcare sectors. In this paper, an Intellectual model of disease diagnosis using the advantage of smart mobile devices has been proposed. This mobile based cancer diagnosis model uses a cloud environment for disease prediction and analysis. The Principal Component Analysis (PCA) technique is utilized to confiscate the superfluous features and choose the most appropriate features. Using the optimized features, cancer disease classification is accomplished using Support Vector Machines with sigmoid kernel function. SVM classifies the patients as normal and abnormal and the evaluated results are conveyed to the patients as well as the respective medical practitioners. The accuracy achieved through proposed model is satisfiable in comparison with other existing methods. Proposed Model incorporates with big data technologies to address the current issues of cancer system.

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