# Two Majority Voting Classifiers Applied to Heart Disease Prediction
## 2 Related Work  
Several studies have applied classification algorithms to the prediction of heart disease. Among them, [1] proposed a'multi-criteria weighted vote-based classifier ensemble' to predict heart disease, while ref. [2] introduced a Chaos Firefly and Rough set-based feature reduction [3] (CFARS-AR) followed by a Fuzzy-Logic (FL) classifier [4] (CFARS-AR FL). Ref. [5] built a clinical decision support system through vote-based classifier ensembles, followed by an ensemble scheme again [6] for heart disease prediction. Another successful application of ensemble learning can be found in [7], where the authors reported a high accuracy for Random Forest. [PERSON] et al. [8] chose a Multilayer Perceptron model to make tests on the Cleveland dataset. [PERSON] et al. [9] analyzed the Fisher score and Matthews correlation to apply feature selection before employing Support Vector Machines (SVM) for the classification. [PERSON] et al. [10] introduced a 'Feature Boundaries Detection' method for one-class classification and conducted experiments on the Spectf, Statlog, and Heart Murmur datasets. Similar to the works of [PERSON] et al. [5; 6], [PERSON] et al. Although these are not 'pure ensemble' algorithms--such as Random Forests or Gradient Boosting Trees-they are ensemble methods and prove the correctness of a claim which can be derived [11]: that is, biomedical data is best analyzed via Ensemble Methods, even though [PERSON] et al. [2], which has been a guide for us, neither proposes an Ensemble Method nor carries out a comparison with Ensemble Methods. Instead, researchers in [2] construct a Fuzzy-Logic Classifier supported by Chaos Firefly and Rough Set Feature Reduction; we favor this hypothesis of Ensemble Methods by introducing a Bagging Classifier of Majority Voting variant weak categorizers. In [12], [PERSON] et al. worked on a 'hybrid technique' consisting of Logistic Regression and ANN, reporting a cross-validation score for the Spectf dataset. [PERSON] et al. [13] studied the effectiveness of Extreme Gradient Boosting [14] when it is backed with DBSCAN outlier removal [15], and SMOTE-ENN balancing [16]. They report a high cross-validation score of 95.90 on the Statlog dataset. This also demonstrates the success of Ensemble Methods on medical datasets. Still, we have instead conducted our experiments according to the train-test split route noted in [2], for it belongs to one of most of the reviews and research on heart disease prediction up to date. The proposed Majority Voting classifier resembles Naive Bayes (NB) ones since it processes the features individually (the difference is that in our approach, the feature classifications are combined through kurtosis + KS test or only KS test weighting). So, we have made an introductory survey of these classifiers. Despite its simplicity, NB Classifier [17] is still used in the literature: from intrusion, detection [18] to the preservation of privacy [19], from fake news detection [20] to emotion recognition [21], it has a wide range of applications. Variants of Naive Bayes are also effective. For example, a compression-based averaging scheme is proposed in [22; 23]. Naive Bayes has been extended to a self-consistent variant to detect masquerades. [24] presents a locally weighted version of Naive Bayes to relax the independence assumption. Kurtosis and Naive Bayes classifiers can be seen in the same context [25], restricted to Independent Component Analysis (ICA) dimension reduction. An example can also be seen in [26] where a 'quantile measure in ICA' is suggested. It is also a candidate for visual or generic feature extraction [27; 28; 29; 30]. The literature on classifiers is still open to development. In addition, there are also works, such as [31], discovering the interpretation of state-of-the-art methods and adapting the classical methods to new systems [32]. To our knowledge, kurtosis has not previously been directly integrated as a weight calculation method in the Majority Voting scheme of Ensemble Methods.