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

Abstract

The availability of a large amount of medical data leads to the need of intelligent disease prediction and analysis tools to extract hidden information. A large number of data mining and statistical analysis tools are used for disease prediction. Single data-mining techniques show acceptable level of accuracy for heart disease diagnosis. This article focuses on prediction and analysis of heart disease using weighted vote-based classifier ensemble technique. The proposed ensemble model overcomes the limitations of conventional data-mining techniques by employing the ensemble of five heterogeneous classifiers: naive Bayes, decision tree based on Gini index, decision tree based on information gain, instance-based learner, and support vector machines. We have used five benchmark heart disease data sets taken from UCI repository. Each data set contains different set of feature space that ultimately leads to the prediction of heart disease. The effectiveness of proposed ensemble classifier is investigated by comparing the performance with different researchers' techniques. Tenfold cross-validation is used to handle the class imbalance problem. Moreover, confusion matrices and analysis of variance statistics are used to show the prediction results of all classifiers. The experimental results verify that the proposed ensemble classifier can deal with all types of attributes and it has achieved the high diagnosis accuracy of 87.37%, sensitivity of 93.75%, specificity of 92.86%, and F -measure of 82.17%. The F -ratio higher than the F -critical and p-value less than 0.01 for a 95% confidence interval indicate that the results are statistically significant for all the data sets.

A hybrid cost-sensitive ensemble for heart disease prediction

“…In addition, KNN is a lazy evaluation method while the other four are eager evaluation methods. Eager algorithm generates frequent itemset rules from a given data set and predicts a class for test instance based on multicriteria approach from selected frequent itemset rules [23]. If no matching is found, default prediction (i.e., the most frequent class in data set) is assigned, which may not be correct.…”

A hybrid cost-sensitive ensemble for heart disease prediction

“…In addition, KNN is a lazy evaluation method while the other four are eager evaluation methods. Eager algorithm generates frequent itemset rules from a given data set and predicts a class for test instance based on multicriteria approach from selected frequent itemset rules [23]. If no matching is found, default prediction (i.e., the most frequent class in data set) is performed, which may not be correct.…”

“…Missing data in medical data sets must be handled carefully because they have a serious effect on the experimental results. Usually, researchers choose to replace the missing values with the mean/mode of the attribute depending on its type [23]. Mokeddem [41] used weighted KNN to calculate the missing values.…”

“…For instance, Das et al [22] proposed a neural networks ensemble and obtained 89.01% classification accuracy from the experiments made on the data taken from Cleveland heart disease dataset. Bashir et al [23] employed the ensemble of five heterogeneous classifiers on five heart disease datasets. The proposed ensemble classifier achieved the high diagnosis accuracy of 87.37%.…”

Voting-Boosting: A novel machine learning ensemble for the prediction of Infants' Data

“…This protocol is used to partition the dataset into k mutually exclusive partitions as the first subset is used as a validation set for training model on the remaining k-1 subset (32) . The overall performance is obtained by averaging the performance of all k subsets and reduces the bias associated with random selection of samples from each data set (33) . In this study, K-fold cross validation with K= 5, 10 and 15 have been used.…”