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Class imbalance and the presence of irrelevant or redundant features in training data can pose serious challenges to the development of a classification framework. This paper proposes a framework for developing a Clinical Decision Support System (CDSS) that addresses class imbalance and the feature selection problem. Under this framework, the dataset is balanced at the data level and a wrapper approach is used to perform feature selection. The following three clinical datasets from the University of California Irvine (UCI) machine learning repository were used for experimentation: the Indian Liver Patient Dataset (ILPD), the Thoracic Surgery Dataset (TSD) and the Pima Indian Diabetes (PID) dataset. The Synthetic Minority Over-sampling Technique (SMOTE), which was enhanced using Orchard's algorithm, was used to balance the datasets. A wrapper approach that uses Chaotic Multi-Verse Optimisation (CMVO) was proposed for feature subset selection. The arithmetic mean of the Matthews correlation coefficient (MCC) and F-score (F1), which was measured using a Random Forest (RF) classifier, was used as the fitness function. After selecting the relevant features, a RF, which comprises 100 estimators and uses the Information Gain Ratio as the split criteria, was used for classification. The classifier achieved a 0.65 MCC, a 0.84 F1 and 82.46% accuracy for the ILPD; a 0.74 MCC, a 0.87 F1 and 86.88% accuracy for the TSD; and a 0.78 MCC, a 0.89 F1and 89.04% accuracy for the PID dataset. The effects of balancing and feature selection on the classifier were investigated and the performance of the framework was compared with the existing works in the literature. The results showed that the proposed framework is competitive in terms of the three performance measures used. The results of a Wilcoxon test confirmed the statistical superiority of the proposed method., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

A framework for clinical diagnosis which uses bioinspired algorithms for feature selection and gradient descendant backpropagation neural network for classification has been designed and implemented. The clinical data are subjected to data preprocessing, feature selection, and classification. Hot deck imputation has been used for handling missing values and min-max normalization is used for data transformation. Wrapper approach that employs bioinspired algorithms, namely, Differential Evolution, Lion Optimization, and Glowworm Swarm Optimization with accuracy of AdaBoostSVM classifier as fitness function has been used for feature selection. Each bioinspired algorithm selects a subset of features yielding three feature subsets. Correlation-based ensemble feature selection is performed to select the optimal features from the three feature subsets. The optimal features selected through correlation-based ensemble feature selection are used to train a gradient descendant backpropagation neural network. Ten-fold cross-validation technique has been used to train and test the performance of the classifier. Hepatitis dataset and Wisconsin Diagnostic Breast Cancer (WDBC) dataset from University of California Irvine (UCI) Machine Learning repository have been used to evaluate the classification accuracy. An accuracy of 98.47% is obtained for Wisconsin Diagnostic Breast Cancer dataset, and 95.51% is obtained for Hepatitis dataset. The proposed framework can be tailored to develop clinical decision-making systems for any health disorders to assist physicians in clinical diagnosis., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this paper., (Copyright © 2019 V. R. Elgin Christo et al.)

Classification is a data mining task, which plays a vital role in clinical diagnosis. Irrelevant features will reduce the classifier accuracy. Only a subset of features in a clinical dataset plays a key role in diagnosing the disease. Thus, selecting the relevant features and training the classifier will improve the classifier accuracy. Clinical datasets are subjected to pre-processing, followed by feature selection and classification. In this work, a framework that uses Synergistic firefly algorithm for feature selection and an ensemble classifier for classification has been designed and implemented. The missing values in the clinical datasets are handled using the k-Nearest Neighbour (k-NN) technique. Min–max normalization is used to normalize the data and the normalized data are split into training and testing sets using tenfold cross-validation. Feature selection has been carried out using a wrapper approach that uses the Synergistic firefly algorithm for generating feature subsets and the Levenberg-Marquardt Back Propagation Neural Network to evaluate the performance of the subsets. The ensemble classifier, consisting of the Levenberg-Marquardt Back Propagation Neural Network, Extreme Learning Machine and Naïve Bayes classifier, is trained using the optimal features selected by the Synergistic firefly algorithm. Experimentation has been carried out using eight clinical datasets from the University of California Irvine (UCI) machine learning repository and it has been inferred that selecting the relevant features improves the classifier accuracy. The results obtained prove the efficiency of the decision-making system developed using Synergistic Firefly and the decision-making system can assist the clinician in decision-making to diagnose diseases. [ABSTRACT FROM AUTHOR]