Computational Intelligence in Healthcare

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

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

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

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

Analysis and data processing to predict myocardial infarction complications

La formació de coàguls a la sang deriva a un infart de miocardi i, generalment, es manifesta de forma inesperada. Aquests coàguls impedeixen la circulació normal de la sang cap al cor i, en conseqüència, les cèl·lules de la zona sense arribar moren, representant un dany irreversible al cor. Aquests pacients són ingressats en estat crític als centres hospitalaris, on la presa de decisions relatives al tractament que cal aplicar va directament relacionada amb les possibilitats de supervivència del pacient. Tenint en compte la quantitat d'analítiques que s'obtenen a l'ingrés, aquest treball té com a objectiu desenvolupar una eina basada en algoritmes de Machine Learning per donar suport a la presa de decisions al personal sanitari que aconsegueixi predir les complicacions derivades de l'infart de miocardi. L’estudi es realitza a partir d’una base de dades obtinguda per S.E. Golovenkin, A.N. Gorban, E.M.Mirkes, V.A. Shulman, D.A. Rossiev, P.A. Shesternya, S.Yu. Nikulina, Yu.V. Orlova, and M.G. Dorrer[1], on s’hi descriuen els paràmetres clínics més característics de 1700 pacients el primer dia d’entrada a l’UCI i un cop transcorregudes les primeres 24, 48 i 72 hores d’admissió a l’hospital. S’ha realitzat un estudi previ de cada una de les variables i, al tractar-se d’una base de dades amb molts valors faltants, ha sigut necessari dur a terme un reprocessament de les dades utilitzant la imputació. D’aquesta manera, ha sigut possible analitzar diferents models i tècniques de remostreig per aconseguir obtenir el millor model de predicció, el qual s’ha acabat evaluant i validant per tal de decidir si pot ser utilitzat per al personal mèdic. Els resultats han resultat ser prometedors atesa la complexitat de modelar la relació dels pacients davant de qualsevol patologia, tenint en compte la gran varietat de factors que influencien en el procés patològic de la malaltia. El bon funcionament d’aquesta eina suposa un gran impacte social ja que permet predir la gravetat d’un infart de miocardi considerant les característiques fenotípiques dels pacients.La formación de coágulos en la sangre deriva a un infarto de miocardio y, generalmente, se manifiesta de forma inesperada. Estos coágulos impiden la circulación normal de la sangre hacia el corazón y, en consecuencia, las células de la zona sin llegar mueren, representando un daño irreversible en el corazón. Estos pacientes son ingresados en estado crítico en los centros hospitalarios, donde la toma de decisiones relativas al tratamiento a aplicar va directamente relacionada con las posibilidades de supervivencia del paciente. Teniendo en cuenta la cantidad de analíticas que se obtienen en el ingreso, este trabajo tiene como objetivo desarrollar una herramienta basada en algoritmos de Machine Learning para apoyar la toma de decisiones al personal sanitario que consiga predecir las complicaciones derivadas del infarto de miocardio. El estudio se realiza a partir de una base de datos obtenida por S.E. Golovenkin, A.N. Gorban, E.M.Mirkes, V.A. Shulman, D.A. Rossiev, P.A. Shesternya, S.Yu. Nikulina, Yu.V. Orlova, and M.G. Dorrer [1], donde se describen los parámetros clínicos más característicos de 1700 pacientes, durante el primer día de entrada en la UCI y una vez transcurridas las primeras 24, 48 y 72 horas de admisión en el hospital. Se ha realizado un estudio previo de cada una de las variables y, al tratarse de una base de datos con muchos valores faltantes, ha sido necesario realizar un reprocesamiento de los datos utilizando la imputación. De esta forma, ha sido posible analizar diferentes modelos y técnicas de remuestreo para conseguir obtener el mejor modelo de predicción, el cual se ha acabado evaluando y validando para decidir si puede ser utilizado para el personal médico. Los resultados han resultado ser prometedores dada la complejidad de modelar la relación de los pacientes ante cualquier patología debido a la gran variedad de factores que influencian en el proceso patológico de la enfermedad. El buen funcionamiento de esta herramienta supone un gran impacto social ya que permite predecir la gravedad de un infarto de miocardio considerando las características fenotípicas de los pacientes.The formation of clots in the blood leads to a myocardial infarction and usually manifests itself unexpectedly. These clots prevent the normal circulation of blood to the heart and, consequently, the cells in the area without reaching it die, representing irreversible damage to the heart. These patients are admitted in critical condition to hospitals, where decision-making regarding the treatment to be applied is directly related to the patient's chances of survival. Taking into account the amount of analytics obtained on admission, this work aims to develop a tool based on Machine Learning algorithms to support decision-making for health personnel, being able to predict the complications derived from myocardial infarction. The study is carried out using a database collected by S.E. Golovenkin, A.N. Gorban, E.M.Mirkes, V.A. Shulman, D.A. Rossiev, P.A. Shesternya, S.Yu. Nikulina, Yu.V. Orlova, and M.G. Dorrer[1], where clinical characteristic parameters from 1700 patients are described on the first day of admission to the ICU and after the first 24, 48 and 72 hospital admission hours. A previous study of each of the variables has been performed and, as it is a database with many missing values, it has been necessary to reprocess the data using imputation. In this way, it has been possible to analyze different models and resampling techniques to obtain the best prediction model, which has been evaluated and validated to decide if it can be used for medical. The results are promising given the complexity of modeling the relationship of patients with any pathology due to the great variety of factors that influence the pathological process of the disease. The proper functioning of this tool has a great social impact since it allows predicting the severity of a myocardial infarction considering the phenotypic characteristics of the patients

High-dimensional and one-class classification

When dealing with high-dimensional data and, in particular, when the number of attributes p is large comparatively to the sample size n, several classification methods cannot be applied. Fisher's linear discriminant rule or the quadratic discriminant one are unfeasible, as the inverse of the involved covariance matrices cannot be computed. A recent approach to overcome this problem is based on Random Projections (RPs), which have emerged as a powerful method for dimensionality reduction. In 2017, Cannings and Samworth introduced the RP method in the ensemble context to extend to the high-dimensional domain classification methods originally designed for low-dimensional data. Although the RP ensemble classifier allows improving classification accuracy, it may still include redundant information. Moreover, differently from other ensemble classifiers (e.g. Random Forest), it does not provide any insight on the actual classification importance of the input features. To account for these aspects, in the first part of this thesis, we investigate two new directions of the RP ensemble classifier. Firstly, combining the original idea of using the Multiplicative Binomial distribution as the reference model to describe and predict the ensemble accuracy and an important result on such distribution, we introduce a stepwise strategy for post-pruning (called Ensemble Selection Algorithm). Secondly, we propose a criterion (called Variable Importance in Projection) that uses the feature coefficients in the best discriminant projections to measure the variable importance in classification. In the second part, we faced the new challenges posed by the high-dimensional data in a recently emerging classification context: one-class classification. This is a special classification task, where only one class is fully known (the target class), while the information on the others is completely missing. In particular, we address this task by using Gini's transvariation probability as a measure of typicality, aimed at identifying the best boundary around the target class

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study

Novel Computationally Intelligent Machine Learning Algorithms for Data Mining and Knowledge Discovery

This thesis addresses three major issues in data mining regarding feature subset selection in large dimensionality domains, plausible reconstruction of incomplete data in cross-sectional applications, and forecasting univariate time series. For the automated selection of an optimal subset of features in real time, we present an improved hybrid algorithm: SAGA. SAGA combines the ability to avoid being trapped in local minima of Simulated Annealing with the very high convergence rate of the crossover operator of Genetic Algorithms, the strong local search ability of greedy algorithms and the high computational efficiency of generalized regression neural networks (GRNN). For imputing missing values and forecasting univariate time series, we propose a homogeneous neural network ensemble. The proposed ensemble consists of a committee of Generalized Regression Neural Networks (GRNNs) trained on different subsets of features generated by SAGA and the predictions of base classifiers are combined by a fusion rule. This approach makes it possible to discover all important interrelations between the values of the target variable and the input features. The proposed ensemble scheme has two innovative features which make it stand out amongst ensemble learning algorithms: (1) the ensemble makeup is optimized automatically by SAGA; and (2) GRNN is used for both base classifiers and the top level combiner classifier. Because of GRNN, the proposed ensemble is a dynamic weighting scheme. This is in contrast to the existing ensemble approaches which belong to the simple voting and static weighting strategy. The basic idea of the dynamic weighting procedure is to give a higher reliability weight to those scenarios that are similar to the new ones. The simulation results demonstrate the validity of the proposed ensemble model