Abstract : One of the most important issues in healthcare and machine learning research is determining the likelihood of organ failure in dengue fever (DF). Researchers have demonstrated the effectiveness of artificial intelligence and machine-learning models in a variety of practical classification problems. The current study focuses on the development of a prediction model for organ failure in dengue. This paper proposes an Enhanced random forest (ERF) model that employs an ensemble of classification methods to achieve this goal. The proposed ERF classifier is tested on a dengue fever dataset collected from dengue patients from all over the West Bengal state in India from 2016 to 2019, from several hospitals, and by interacting with people previously infected with DF individually using online and offline questionnaire methods. The proposed classifier is also compared to some cutting-edge machine-learning classifiers, including random forest, naive Bayes, support vector machine with radial basis function kernel, and decision tree. To assess the strength of the proposed ERF classifier, various performance metrics such as accuracy, sensitivity, specificity, receiver operating characteristic, area under the curve, and some statistical tests such as kappa statistics were used to test the classifiers. To test the credibility of the classification models in dealing with unbalanced data, various splits of training and testing data – namely, 50–50 percent, 66–34 percent, 80–20 percent, and 10-fold cross-validation – were used in this study. The output results were also compared to previous research on the same dataset, where the proposed classifiers were found to be the best across all performance dimensions.

Keywords: Enhanced Random Forest (ERF), Dengue Fever (DF), Organ Failure, Ensemble of Classification Methods, Kappa Statistic, ROC-AUC

| DOI: 10.17148/IARJSET.2021.8816