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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]

A framework for Computer-Aided Diagnosis (CAD) to diagnose Cavitary TB and Miliary TB from chest Computed Tomography (CT) slices has been designed and implemented. The lung tissues from the CT slices are segmented using region-based Active Contour Model (ACM) and the Region of Interests (ROIs) labelled by an expert radiologist are extracted. Features based on shape and texture are extracted from each ROI. A wrapper-based Improved Artificial Bee Colony Optimization (I-ABCO) algorithm with the accuracy of the Support Vector Machine (SVM) classifier as the fitness function is used to select the optimal subset of features. The search process of I-ABCO is improved using two evaluation functions, namely, rough dependency measure (RDM) and mutual information (MI), to promote better exploitation of the search space. The selected features are used to train the Radial Basis Function Neural Network (RBFNN) classifier, using ten-fold cross validation and the performance is evaluated. Experimentation has been performed on CT slices using two datasets: Tuberculosis dataset and Lung Image Database Consortium-Image Database Resource Initiative (LIDC-IDRI) dataset. The accuracy of the CAD systems using I-ABCO with MI and I-ABCO with RDM for both datasets are (88.34%, 92.63%) and (87.32%, 90.17%), respectively. The CAD system using the basic Artificial Bee Colony Optimization (ABCO) algorithm for feature selection is also experimented with the same datasets and an accuracy of (85.68%, 88%) is achieved. The performance of the proposed algorithms outperforms the ABCO algorithm in diagnostic accuracy, which ensures that selecting the optimal feature subset efficiently has an impact on the classification accuracy. [ABSTRACT FROM AUTHOR]

• A CDSS that uses Diverse Intensified Strawberry Optimized Neural Network is proposed. • Feature importance computed using ERT classifier is used for feature selection. • The two phase training prevents the neural network from getting stuck in local optima. • FFANN is trained using a Strawberry Plant Optimization and Back Propagation algorithm. • The proposed method can serve as a second opinion to physicians in making decisions. A novel classification framework for clinical decision making that uses an Extremely Randomized Tree (ERT) based feature selection and a Diverse Intensified Strawberry Optimized Neural network (DISON) is proposed. DISON is a Feed Forward Artificial Neural Network where the optimization of weights and bias is done using a two phase training strategy. Two algorithms namely Strawberry Plant Optimization (SPO) algorithm and Gradient-descent Back-propagation algorithm are used sequentially to identify the optimum weights and bias. The novel two phase training method and the stochastic duplicate-elimination strategy of SPO helps in addressing the issue of local optima associated with conventional neural networks. The relevant attributes are selected based on the feature importance values computed using an ERT classifier. Vertebral Column, Pima Indian diabetes (PID), Cleveland Heart disease (CHD) and Statlog Heart disease (SHD) datasets from the University of California Irvine machine learning repository are used for experimentation. The framework has achieved an accuracy of 87.17% for Vertebral Column, 90.92% for PID, 93.67% for CHD and 94.5% for SHD. The classifier performance has been compared with existing works and is found to be competitive in terms of accuracy, sensitivity and specificity. Wilcoxon test confirms the statistical superiority of the proposed method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]