An ensemble data mining approach to discover medical patterns and provide a system to predict the mortality in the ICU of cardiac surgery based on stacking machine learning method

Abstract

Nowadays, according to spectacular improvement in health care and biomedical level, a tremendous amount of data is recorded by hospitals. In addition, the most effective approach to reduce disease mortality is to diagnose it as soon as possible. As a result, data mining by applying machine learning in the field of diseases provides good opportunities to examine the hidden patterns of this collection. An exact forecast of the mortality after heart surgery will cause Successful medical treatment and fewer costs. This research wants to recommend a new stacking predictive model after utilizing the random forest feature importance method to foresee the mortality after heart surgery on a highly unbalanced dataset by using the most practical features. To solve the unbalanced data problem, a combination of the SVM-SMOTE over-sampling algorithm and the Edited-Nearest-Neighbor undersampling algorithm is used. This research compares the introduced model with some other machine learning classifiers to ensure efficiency through shuffle hold-out and 10-fold cross-validation strategies. In order to validate the performance of the implemented machine learning methods in this research, both shuffle hold-out, and 10-fold cross-validation results indicated that our model had the highest efficiency compared to the other models. Furthermore, the Friedman statistical test is applied to survey the differences between models. The result demonstrates that the introduced stacking model reached the most accurate predicting performance after Logistic Regression.

A Scalable Machine Learning-based Ensemble Approach to Enhance the Prediction Accuracy for Identifying Students at-Risk

“…In the case of a large sample size, the number of students at risk will be significantly lower, and hence, in such situations of highly imbalanced data, the present model may be quite useful. The highest prediction accuracy achieved in the present study is 95.45%, which is greater than most of the previous studies [12][13][14][15][16][17][18]. Along with the enhanced prediction accuracy, the main advantage of the present work is that the methodology proposed in the present study is scalable from one context to the other.…”

“…Ghorbani and Ghousi [15] used and compared different resampling methods, viz., Borderline SMOTE, Random Over Sampler, SMOTE, SMOTE-ENN, SVM-SMOTE, and SMOTE-Tomek, by evaluating the performance of the various classifiers, and Random Forest obtained the highest accuracy of 81.27% with SVM-SMOTE. Further, Ghavidel et al [16] solved the problem of imbalanced data by using a combination of the SVM-SMOTE (an over-sampling technique) and Edited-Nearest-Neighbor (an under-sampling technique) while predicting disease mortality. Recently, Desiani et al [17] applied k-Nearest Neighbor (k-NN), Artificial Neural Network (ANN), and C4.5 to students" educational background records along with SMOTE to make the dataset balanced, and that balanced dataset increased the accuracy of prediction, and for k-NN the maximum achieved accuracy was 83.71%.…”

Artificial Intelligence–enabled Decision Support in Surgery

“…Twentythree articles 9,19,21,[23][24][25]27,29,30,[32][33][34]36,37,39,41,42,[44][45][46][48][49][50] (63.9%) reported precision metrics (area under the precision-recall curve, positive predictive value, or F1 score). Twenty-five articles 9,20,21,[23][24][25][26][27][28]31,33,34,36,38,40,[42][43][44][45][46][47][48][49][50] (69.4%) included explainability mechanisms to convey the relative importance of input features in determining outputs. Thirteen articles 9,16,17,…”

“…None of the articles described equity analyses in which model performance was stratified by sex or race. Twenty-three articles9,19,21,23–25,27,29,30,32–34,36,37,39,41,42,44–46,48–50 (63.9%) reported precision metrics (area under the precision-recall curve, positive predictive value, or F1 score). Twenty-five articles9,20,21,23–28,31,33,34,36,38,40,42–50 (69.4%) included explainability mechanisms to convey the relative importance of input features in determining outputs.…”

“…Twenty-three articles9,19,21,23–25,27,29,30,32–34,36,37,39,41,42,44–46,48–50 (63.9%) reported precision metrics (area under the precision-recall curve, positive predictive value, or F1 score). Twenty-five articles9,20,21,23–28,31,33,34,36,38,40,42–50 (69.4%) included explainability mechanisms to convey the relative importance of input features in determining outputs. Thirteen articles9,16,17,20,25,27,29,30,35,38,45,46,50 (36.1%) presented a framework that could be used for clinical implementation; none of the articles assessed the efficacy of clinical implementation.…”