BagMOOV: A novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting

Abstract

Conventional clinical decision support systems are based on individual classifiers or simple combination of these classifiers which tend to show moderate performance. This research paper presents a novel classifier ensemble framework based on enhanced bagging approach with multi-objective weighted voting scheme for prediction and analysis of heart disease. The proposed model overcomes the limitations of conventional performance by utilizing an ensemble of five heterogeneous classifiers: Naïve Bayes, linear regression, quadratic discriminant analysis, instance based learner and support vector machines. Five different datasets are used for experimentation, evaluation and validation. The datasets are obtained from publicly available data repositories. Effectiveness of the proposed ensemble is investigated by comparison of results with several classifiers. Prediction results of the proposed ensemble model are assessed by ten fold cross validation and ANOVA statistics. The experimental evaluation shows that the proposed framework deals with all type of attributes and achieved high diagnosis accuracy of 84.16 %, 93.29 % sensitivity, 96.70 % specificity, and 82.15 % f-measure. The f-ratio higher than f-critical and p value less than 0.05 for 95 % confidence interval indicate that the results are extremely statistically significant for most of the datasets.

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization

“…The ranges of improvement shown by MLP in the objective specificity are −9.68 to 86.33 and −18.93 to 36.33 without and with resampling, respectively. From the experimental results, it is concluded that FMLP shows outperformance than recently developed ensemble classifiers ([14, 15]). As a part of the continuation of this research, we intend to process a very higher dimensional dataset with the major phases of feature selection and parameter evolution of the classifier.…”

“…References [14] through [16] are recent literature, and they have been referred to in our work as the base papers for every dataset (as has been earmarked in legends in Figure 2). The results of datasets corresponding to diseases like breast cancer, hepatitis, BUPA liver, Pima, Cleveland, and Parkinson have been compared with those of [14], whereas the results of Statlog, Spect, Spectf, and Eric have been compared with those of BagMOOV [15]. Thyroid disease results alone are compared with those of [16].…”

Analysis of Supervised Machine Learning Algorithms for Heart Disease Prediction with Reduced Number of Attributes using Principal Component Analysis

“…Similarly, several techniques had been used [18] [19] to improve the accuracy of the classifiers for better prediction. Some of them are tabulated in Table 3.…”

Automated Disease Prediction System (ADPS): A User Input-based Reliable Architecture for Disease Prediction

“…A lot of effort has been put on to predict specific diseases [7], [8]. For instance, authors in [7] focus on predicting coronary heart diseases by mining text.…”