Optimal Mean Arterial Pressure for Favorable Neurological Outcomes in Survivors after Extracorporeal Cardiopulmonary Resuscitation

We evaluated the optimal mean arterial pressure (MAP) for favorable neurological outcomes in patients who underwent extracorporeal cardiopulmonary resuscitation (ECPR). Adult patients who underwent ECPR were included. The average MAP was obtained during 6, 12, 24, 48, 72, and 96 h after cardiac arrest, respectively. Primary outcome was neurological status upon discharge, as assessed by the Cerebral Performance Categories (CPC) scale (range from 1 to 5). Overall, patients with favorable neurological outcomes (CPC 1 or 2) tended to have a higher average MAP than those with poor neurological outcomes. Six models were established based on ensemble algorithms for machine learning, multiple logistic regression and observation times. Patients with average MAP around 75 mmHg had the least probability of poor neurologic outcomes in all the models. However, those with average MAPs below 60 mmHg had a high probability of poor neurological outcomes. In addition, based on an increase in the average MAP, the risk of poor neurological outcomes tended to increase in patients with an average MAP above 75 mmHg. In this study, average MAPs were associated with neurological outcomes in patients who underwent ECPR. Especially, maintaining the survivor’s MAP at about 75 mmHg may be important for neurological recovery after ECPR.

Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers

In the past few years, educational data mining (EDM) has attracted the attention of researchers to enhance the quality of education. Predicting student academic performance is crucial to improving the value of education. Some research studies have been conducted which mainly focused on prediction of students’ performance at higher education. However, research related to performance prediction at the secondary level is scarce, whereas the secondary level tends to be a benchmark to describe students’ learning progress at further educational levels. Students’ failure or poor grades at lower secondary negatively impact them at the higher secondary level. Therefore, early prediction of performance is vital to keep students on a progressive track. This research intended to determine the critical factors that affect the performance of students at the secondary level and to build an efficient classification model through the fusion of single and ensemble-based classifiers for the prediction of academic performance. Firstly, three single classifiers including a Multilayer Perceptron (MLP), J48, and PART were observed along with three well-established ensemble algorithms encompassing Bagging (BAG), MultiBoost (MB), and Voting (VT) independently. To further enhance the performance of the abovementioned classifiers, nine other models were developed by the fusion of single and ensemble-based classifiers. The evaluation results showed that MultiBoost with MLP outperformed the others by achieving 98.7% accuracy, 98.6% precision, recall, and F-score. The study implies that the proposed model could be useful in identifying the academic performance of secondary level students at an early stage to improve the learning outcomes.

Analysis on Ensemble Methods for the Prediction of Cardiovascular Disease

Coronary Heart disease is one of the dominant sources of death and morbidity for the people worldwide. The identification of cardiac disease in the clinical review is considered one of the main problems. As the amount of data grows increasingly, interpretation and retrieval become even more complex. In addition, the Ensemble learning prediction model seems to be an important fact in this area of study. The prime aim of this paper is also to forecast CHD accurately. This paper is intended to offer a modern paradigm for prediction of cardiovascular diseases with the use of such processes such as pre-processing, detection of features, feature selection and classification. The pre-processing will initially be performed using the ordinal encoding technique, and the statistical and the features of higher order are extracted using the Fisher algorithm. Later, the minimization of record and attribute is performed, in which principle component analysis performs its extensive part in figuring out the “curse of dimensionality.” Lastly, the process of prediction is carried out by the different Ensemble models (SVM, Gaussian Naïve Bayes, Random forest, K-nearest neighbor, Logistic regression, decision tree and Multilayer perceptron that intake the features with reduced dimensions. Finally, in comparison to such success metrics the reliability of the proposal work is compared and its superiority has been confirmed. From the analysis, Naïve bayes with regards to accuracy is 98.4% better than other Ensemble algorithms.