Feature Selection for Text and Image Data Using Differential Evolution with SVM and Naïve Bayes Classifiers

Classification problems are increasing in various important applications such as text categorization, images, medical imaging diagnosis and bimolecular analysis etc. due to large amount of attribute set. Feature extraction methods in case of large dataset play an important role to reduce the irrelevant feature and thereby increases the performance of classifier algorithm. There exist various methods based on machine learning for text and image classification. These approaches are utilized for dimensionality reduction which aims to filter less informative and outlier data. Therefore, these approaches provide compact representation and computationally better tractable accuracy. At the same time, these methods can be challenging if the search space is doubled multiple time. To optimize such challenges, a hybrid approach is suggested in this paper. The proposed approach uses differential evolution (DE) for feature selection with naïve bayes (NB) and support vector machine (SVM) classifiers to enhance the performance of selected classifier. The results are verified using text and image data which reflects improved accuracy compared with other conventional techniques. A 25 benchmark datasets (UCI) from different domains are considered to test the proposed algorithms.  A comparative study between proposed hybrid classification algorithms are presented in this work. Finally, the experimental result shows that the differential evolution with NB classifier outperforms and produces better estimation of probability terms. The proposed technique in terms of computational time is also feasible

Ensemble methods for meningitis aetiology diagnosis

In this work, we explore data-driven techniques for the fast and early diagnosis concerning the etiological origin of meningitis, more specifically with regard to differentiating between viral and bacterial meningitis. We study how machine learning can be used to predict meningitis aetiology once a patient has been diagnosed with this disease. We have a dataset of 26,228 patients described by 19 attributes, mainly about the patient's observable symptoms and the early results of the cerebrospinal fluid analysis. Using this dataset, we have explored several techniques of dataset sampling, feature selection and classification models based both on ensemble methods and on simple techniques (mainly, decision trees). Experiments with 27 classification models (19 of them involving ensemble methods) have been conducted for this paper. Our main finding is that the combination of ensemble methods with decision trees leads to the best meningitis aetiology classifiers. The best performance indicator values (precision, recall and f-measure of 89% and an AUC value of 95%) have been achieved by the synergy between bagging and NBTrees. Nonetheless, our results also suggest that the combination of ensemble methods with certain decision tree clearly improves the performance of diagnosis in comparison with those obtained with only the corresponding decision tree.This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. We would like to thank the Health Department of the Brazilian Government for providing the dataset and for authorizing its use in this study. We would also like to express our gratitude to the reviewers for their thoughtful comments and efforts towards improving our manuscript. Funding for open access charge: Universidad de Málaga / CBUA

An Ensemble Framework Coping with Instability in the Gene Selection Process

[EN] This paper proposes an ensemble framework for gene selection, which is aimed at addressing instability problems presented in the gene filtering task. The complex process of gene selection from gene expression data faces different instability problems from the informative gene subsets found by different filter methods. This makes the identification of significant genes by the experts difficult. The instability of results can come from filter methods, gene classifier methods, different datasets of the same disease and multiple valid groups of biomarkers. Even though there is a wide number of proposals, the complexity imposed by this problem remains a challenge today. This work proposes a framework involving five stages of gene filtering to discover biomarkers for diagnosis and classification tasks. This framework performs a process of stable feature selection, facing the problems above and, thus, providing a more suitable and reliable solution for clinical and research purposes. Our proposal involves a process of multistage gene filtering, in which several ensemble strategies for gene selection were added in such a way that different classifiers simultaneously assess gene subsets to face instability. Firstly, we apply an ensemble of recent gene selection methods to obtain diversity in the genes found (stability according to filter methods). Next, we apply an ensemble of known classifiers to filter genes relevant to all classifiers at a time (stability according to classification methods). The achieved results were evaluated in two different datasets of the same disease (pancreatic ductal adenocarcinoma), in search of stability according to the disease, for which promising results were achieved

A comprehensive study on disease risk predictions in machine learning

Over recent years, multiple disease risk prediction models have been developed. These models use various patient characteristics to estimate the probability of outcomes over a certain period of time and hold the potential to improve decision making and individualize care. Discovering hidden patterns and interactions from medical databases with growing evaluation of the disease prediction model has become crucial. It needs many trials in traditional clinical findings that could complicate disease prediction. Comprehensive survey on different strategies used to predict disease is conferred in this paper. Applying these techniques to healthcare data, has improvement of risk prediction models to find out the patients who would get benefit from disease management programs to reduce hospital readmission and healthcare cost, but the results of these endeavours have been shifted

Computational Intelligence in Healthcare

This book is a printed edition of the Special Issue Computational Intelligence in Healthcare that was published in Electronic

Computational Intelligence in Healthcare

The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications