Evolutionary Computation for Optimal Ensemble Classifier in Lymphoma Cancer Classification

Abstract. Owing to the development of DNA microarray technologies, it is possible to get thousands of expression levels of genes at once. If we make the effective classification system with such acquired data, we can predict the class of new sample, whether it is normal or patient. For the classification system, we can use many feature selection methods and classifiers, but a method cannot be superior to the others absolutely for feature selection or classification. Ensemble classifier has been using to yield improved performance in this situation, but it is almost impossible to get all ensemble results, if there are many feature selection methods and classifiers to be used for ensemble. In this paper, we propose GA based method for searching optimal ensemble of feature-classifier pairs on Lymphoma cancer dataset. We have used two ensemble methods, and GA finds optimal ensemble very efficiently.

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Bayesian learning with local support vector machines for cancer classification with gene expression data

Contains fulltext : 84537.pdf (author's version ) (Open Access)Applications of Evolutionary Computin

High-Dimensional Micro-array Data Classification Using Minimum Description Length and Domain Expert Knowledge

This paper reports on three machine learning methods, i.e. Naïve Bayes (NB), Adaptive Bayesian Network (ABN) and Support Vector Machines (SVM) for multi-target classification on micro-array datasets involving a large feature space and very few samples. By adopting the Minimum Description Length criterion for ranking and selecting relevant features, experiments are carried out to investigate the accuracy and effectiveness of the above methods in classifying many targets as well as to study the effects of feature selection on the sensitivity of each classifier. The paper also shows how the knowledge of a domain expert makes it possible to decompose the multi-target classification in a set of binary classifications, one for each target, with a substantial improvement in accuracy. The effectiveness of the MDL criterion to decide on particular feature subsets is asserted by empirical results showing that MDL is comparable with entropy based feature selection methodologies reported by earlier works

Impaired lung function and Health Status in Adult Survivors of Bronchopulmonary Dysplasia

[Abstract]: In recent years, the rapid development of DNA Microarray technology has made it possible for scientists to monitor the expression level of thousands of genes in a single experiment. As a new technology, Microarray data presents some fresh challenges to scientists since Microarray data contains a large number of genes (around tens thousands) with a small number of samples (around hundreds). Both filter and wrapper gene selection methods aim to select the most informative genes among the massive data in order to reduce the size of the expression database. Gene selection methods are used in both data preprocessing and classification stages. We have conducted some experiments on different existing gene selection methods to preprocess Microarray data for classification by benchmark algorithms SVMs and C4.5. The study suggests that the combination of filter and wrapper methods in general improve the accuracy performance of gene expression Microarray data classification. The study also indicates that not all filter gene selection methods help improve the performance of classification. The experimental results show that among tested gene selection methods, Correlation Coefficient is the best gene selection method for improving the classification accuracy on both SVMs and C4.5 classification algorithms