A hybrid cost-sensitive ensemble for heart disease prediction

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.

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A hybrid cost-sensitive ensemble for heart disease prediction

10.21203/rs.2.22946/v4 ◽

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.

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A hybrid cost-sensitive ensemble for heart disease prediction

10.21203/rs.2.22946/v5 ◽

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.

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A hybrid cost-sensitive ensemble for heart disease prediction

10.21203/rs.2.22946/v3 ◽

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.

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A hybrid cost-sensitive ensemble for heart disease prediction

10.21203/rs.2.22946/v2 ◽

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.

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A hybrid cost-sensitive ensemble for heart disease prediction

10.21203/rs.2.22946/v1 ◽

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.

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A Survey on Blood Image Diseases Detection Using Deep Learning

International Journal of Service Science Management Engineering and Technology ◽

10.4018/ijssmet.2020070102 ◽

2020 ◽

Vol 11 (3) ◽

pp. 18-32

Author(s):

Mohamed Loey ◽

Mukdad Rasheed Naman ◽

Hala Helmy Zayed

Keyword(s):

Deep Learning ◽

Blood Cells ◽

Nearest Neighbor ◽

Disease Diagnosis ◽

Research Area ◽

Support Vector ◽

K Nearest Neighbor ◽

Blood Disease ◽

Active Research ◽

Time Accuracy

Blood disease detection and diagnosis using blood cells images is an interesting and active research area in both the computer and medical fields. There are many techniques developed to examine blood samples to detect leukemia disease, these techniques are the traditional techniques and the deep learning (DL) technique. This article presents a survey on the different traditional techniques and DL approaches that have been employed in blood disease diagnosis based on blood cells images and to compare between the two approaches in quality of assessment, accuracy, cost and speed. This article covers 19 studies, 11 of these studies were in traditional techniques which used image processing and machine learning (ML) algorithms such as K-means, K-nearest neighbor (KNN), Naïve Bayes, Support Vector Machine (SVM), and 8 studies in advanced techniques which used DL, particularly Convolutional Neural Networks (CNNs) which is the most widely used in the field of blood image diseases detection since it is highly accurate, fast, and has the least cost. In addition, it analyzes a number of recent works that have been introduced in the field including the size of the dataset, the used methodologies, the obtained results, etc. Finally, based on the conducted study, it can be concluded that the proposed system CNN was achieving huge successes in the field whether regarding features extraction or classification task, time, accuracy, and had a lower cost in the detection of leukemia diseases.

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A Systematic Methodology to Evaluate Prediction Models for Driving Style Classification

Sensors ◽

10.3390/s20061692 ◽

2020 ◽

Vol 20 (6) ◽

pp. 1692 ◽

Cited By ~ 6

Author(s):

Iván Silva ◽

José Eugenio Naranjo

Keyword(s):

Machine Learning ◽

Nearest Neighbor ◽

Performance Metrics ◽

Prediction Models ◽

Statistical Tests ◽

Area Under The Curve ◽

The Other ◽

Support Vector ◽

Classification Models ◽

K Nearest Neighbor

Identifying driving styles using classification models with in-vehicle data can provide automated feedback to drivers on their driving behavior, particularly if they are driving safely. Although several classification models have been developed for this purpose, there is no consensus on which classifier performs better at identifying driving styles. Therefore, more research is needed to evaluate classification models by comparing performance metrics. In this paper, a data-driven machine-learning methodology for classifying driving styles is introduced. This methodology is grounded in well-established machine-learning (ML) methods and literature related to driving-styles research. The methodology is illustrated through a study involving data collected from 50 drivers from two different cities in a naturalistic setting. Five features were extracted from the raw data. Fifteen experts were involved in the data labeling to derive the ground truth of the dataset. The dataset fed five different models (Support Vector Machines (SVM), Artificial Neural Networks (ANN), fuzzy logic, k-Nearest Neighbor (kNN), and Random Forests (RF)). These models were evaluated in terms of a set of performance metrics and statistical tests. The experimental results from performance metrics showed that SVM outperformed the other four models, achieving an average accuracy of 0.96, F1-Score of 0.9595, Area Under the Curve (AUC) of 0.9730, and Kappa of 0.9375. In addition, Wilcoxon tests indicated that ANN predicts differently to the other four models. These promising results demonstrate that the proposed methodology may support researchers in making informed decisions about which ML model performs better for driving-styles classification.

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Heart Disease Prediction using Neural Network

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35418 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 1907-1910

Author(s):

Ganesh Nanekar

Keyword(s):

Heart Disease ◽

Nearest Neighbor ◽

Heart Diseases ◽

Machine Learning Techniques ◽

Whole Body ◽

Support Vector ◽

K Nearest Neighbor ◽

Monthly Basis ◽

Related Data ◽

Learning Techniques

Heart is the next major organ comparing to brain which has more priority in Human body. It pumps the blood and supplies to all organs of the whole body. Prediction of occurrences of heart diseases in medical field is significant work. Data analytics is useful for prediction from more information and it helps medical Centre to predict of various disease. Huge amount of patient related data is maintained on monthly basis. The stored data can be useful for source of predicting the occurrence of future disease. Some of the data mining and machine learning techniques are used to predict the heart disease, such as Decision tree, Fuzzy Logic, K-Nearest Neighbor (KNN), Naïve Bayes and Support Vector Machine (SVM). This paper provides an insight of the existing algorithms and implements hybrid algorithms to improve accuracy significantly.

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New hybrid method for heart disease diagnosis utilizing optimization algorithm in feature selection

Health and Technology ◽

10.1007/s12553-019-00396-3 ◽

2019 ◽

Vol 10 (3) ◽

pp. 667-678 ◽

Cited By ~ 3

Author(s):

Jalil Nourmohammadi-Khiarak ◽

Mohammad-Reza Feizi-Derakhshi ◽

Khadijeh Behrouzi ◽

Samaneh Mazaheri ◽

Yashar Zamani-Harghalani ◽

...

Keyword(s):

Feature Selection ◽

Heart Disease ◽

Nearest Neighbor ◽

Imperialist Competitive Algorithm ◽

Disease Diagnosis ◽

K Nearest Neighbor ◽

Feature Selection Technique ◽

Medical Databases ◽

Classification Evaluation ◽

Heart Disease Diagnosis

AbstractThe number and size of medical databases are rapidly increasing, and the advanced models of data mining techniques could help physicians to make efficient and applicable decisions. The challenges of heart disease data include the feature selection, the number of the samples; imbalance of the samples, lack of magnitude for some features, etc. This study mainly focuses on the feature selection improvement and decreasing the numbers of the features. In this study, imperialist competitive algorithm with meta-heuristic approach is suggested in order to select prominent features of the heart disease. This algorithm can provide a more optimal response for feature selection toward genetic in compare with other optimization algorithms. Also, the K-nearest neighbor algorithm is used for the classification. Evaluation result shows that by using the proposed algorithm, the accuracy of feature selection technique has been improved.

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