An Effective Heart Disease Prediction Framework based on Ensemble Techniques in Machine Learning

Abstract

To design a framework for effective prediction of heart disease based on ensemble techniques, without the need of feature selection, incorporating data balancing, outlier detection and removal techniques, with results that are still at par with cutting-edge research. In this study, the Cleveland dataset, which has 303 occurrences, is used from the UCI repository. The dataset comprises 76 raw attributes, however only 14 of them are listed by the UCI repository as significant risk factors for heart disease when the dataset is uploaded as an open source dataset. Data balancing strategies, such as random over sampling, are used to address the issue of unbalanced data. Additionally, an isolation forest is used to find outliers in multivariate data, which has not been explored in previous research. After eliminating anomalies from the data, ensemble techniques such as bagging, boosting, voting, stacking are employed to create the prediction model. The potential of the proposed model is assessed for accuracy, sensitivity, and specificity, positive prediction value (PPV), negative prediction value (NPV), F1 score, ROC-AUC and model training time. For the Cleveland dataset, the performance of the suggested methodology is superior, with 98.73% accuracy, 98% sensitivity, 100% specificity, 100% PPV, 97% NPV, 1 as F score, and AUC as 1 with comparatively very less training time. The results of this study demonstrate that our proposed approach significantly outperforms the existing scholarly work in terms of accuracy and all the stated performance metrics. No earlier research has focused on these many performance parameters.

An Improved Ensemble-Based Cardiovascular Disease Detection System with Chi-Square Feature Selection

“…Through bootstrapping, distinct subsets of the training data are created by random sampling with replacement from the original dataset. This helps in reducing overfitting by providing a more robust and generalizable model [42,43]. At each node of the decision tree, a random subset of features is considered for each split instead of using all features for each split.…”

“…The RF algorithm involves the tuning of multiple hyperparameters, including the n estimators (count of decision trees for growth in the forest), max depth of each decision tree in the forest, criterion (a function used to evaluate split quality at each node of the trees, for example, Gini impurity and information entropy) [42]. More trees in the forest generally results in more reliable and accurate predictions [43].…”

“…Such a classifier demonstrates its versatility by not only combining different models but also allowing for both hard and soft voting mechanisms. In hard voting, each base classifier votes for a single class label, and the class with most votes is selected as the final output prediction [43]. This is a simple majority voting method.…”

“…In this case, if two models indicate that class A is the most likely outcome, and the third model indicates that class B is more likely, then class A will be used [40]. In contrast, soft voting takes a more probabilistic approach, averaging the probability estimations provided by each base classifier for each class label and selecting the one with the biggest average probability as the final output prediction [43].…”

Enhanced cardiovascular disease prediction through self-improved Aquila optimized feature selection in quantum neural network & LSTM model

“…In their study, Yewale et al ( 16 ) devised a comprehensive framework for predicting cardiovascular disease. They made a deliberate choice to exclude FS and instead focused on data balance and outlier identification.…”