Alexandros G. Rigas

An Intelligent System Approach for Asthma Prediction in Symptomatic Preschool Children

Objectives. In this study a new method for asthma outcome prediction, which is based on Principal Component Analysis and Least Square Support Vector Machine Classifier, is presented. Most of the asthma cases appear during the first years of life. Thus, the early identification of young children being at high risk of developing persistent symptoms of the disease throughout childhood is an important public health priority. Methods. The proposed intelligent system consists of three stages. At the first stage, Principal Component Analysis is used for feature extraction and dimension reduction. At the second stage, the pattern classification is achieved by using Least Square Support Vector Machine Classifier. Finally, at the third stage the performance evaluation of the system is estimated by using classification accuracy and 10-fold cross-validation. Results. The proposed prediction system can be used in asthma outcome prediction with 95.54 % success as shown in the experimental results. Conclusions. This study indicates that the proposed system is a potentially useful decision support tool for predicting asthma outcome and that some risk factors enhance its predictive ability.

Statistical methods and software for risk assessment

Different statistical methods for the assessment of potential risk factors are discussed, in the case of a complex neurophysiological system, involving binary observations. The first approach describes the use of non-parametric methods, which are based on the cross-product ratio (CPR). The estimates of the CPR are given both in time and frequency domains and their asymptotic distributions are discussed. The second approach is a parametric one and is based on the formulation of a logistic regression model. The estimated parameters and the corresponding odds ratio (OR) can be used to evaluate the behaviour of the system. These methods can be implemented using different statistical software, e.g. S-PLUS, GENSTAT, SPSS, LOGXACT, GLIM. Some of the computational procedures are provided, and the results obtained are clearly displayed. A discussion about the statistical packages is presented.

An Artificial intelligence technique for the prediction of persistent asthma in children

The prediction of asthma that persists throughout childhood and into adulthood, in early life of a child has practical, clinical and prognostic implications and sets the basis for the future prevention. Artificial Neural Networks (ANNs) seems to be a superior tool for analyzing data sets where nonlinear relationships are existing between the input data and the predicted output. This study presents an effective machine-learning approach based on Multi-Layer Perceptron (MLP) neural networks, for the prediction of persistent asthma in children. Through a feature reduction, 10 high importance prognostic factors correlated to persistent asthma have been discovered. The feature selection approach results in 89.8% reduction of the initial number of features. Afterwards, a feature reduced classifier is constructed, which achieves 100% accuracy on the training and test data sets. Experimental results are presenting and verify this statement.

Diagnosis of Asthma Severity Using Artificial Neural Networks

During the last years, neural networks have become a very important method in the field of medical diagnostic. In this work, a technique is proposed that involves training a Multi-Layer Perceptron with back-propagation learning algorithm, in order to recognize three classes of asthma severity, through the results of breathing tests. The breathing test parameters and the diagnosis of physicians for 200 cases of children- patients, aged 10-12 years from Alexandroupolis Hospital in Greece, are used in the supervised training method to update the network parameters. This method was implemented to diagnose three asthma cases according to their severity: mild, moderate and severe asthma. Results obtained by using Neural Network Toolbox of Matlab, show that the proposed ANN can be used in asthma diagnosis with 98% success. This research work improves the asthma diagnosis accuracy with higher consistency in order to specify the seriousness of the condition of a patient and the appropriate course of medical treatment.

An Evolutionary Two-Objective Genetic Algorithm for Asthma Prediction

Genetic Algorithms in combination with Artificial Neural Networks have been used to solve optimization problems in several domains. In this paper, an evolutionary algorithm consisting of an Artificial Neural Network and a Genetic Algorithm is presented for predicting the asthma outcome in children under the age of five. The most cases of asthma begin during the first years of life, thus the early determination of which young children will have asthma later in their life counts as an important priority. A Genetic algorithm search is implemented in order to investigate which prognostic factors contribute most to the asthma prediction. This search results to pruned input and hidden layers of the Artificial Neural Network as well as minimization of the Mean Square Error of the trained network at the test phase. Thus, dimension reduction of the prognostic factors can be achieved without any loss of prediction ability.

Predicting asthma outcome using partial least square regression and artificial neural networks

The long-termsolution to the asthma epidemic is believed to be prevention and not treatment of the established disease. Most cases of asthma begin during the first years of life; thus the early determination of which young children will have asthma later in their life counts as an important priority. Artificial neural networks (ANN) have been already utilized in medicine in order to improve the performance of the clinical decision-making tools. In this study, a new computational intelligence technique for the prediction of persistent asthma in children is presented. By employing partial least square regression, 9 out of 48 prognostic factors correlated to the persistent asthma have been chosen. Multilayer perceptron and probabilistic neural networks topologies have been investigated in order to obtain the best prediction accuracy. Based on the results, it is shown that the proposed system is able to predict the asthma outcome with a success of 96.77%. The ANN, with which these high rates of reliability were obtained, will help the doctors to identify which of the young patients are at a high risk of asthma disease progression. Moreover, thismay lead to better treatment opportunities and hopefully better disease outcomes in adulthood.

Measuring the association of stationary point processes using spectral analysis techniques

In this work we focus on relationships between stationary point process using spectral analysis techniques. The evaluation of these relationships is accomplished with the help of the product ratio of association (PRA), which is based on the cumulant densities of the point processes. The estimation procedure is obtained by smoothing the periodogram statistic, a function of the frequency domain. It is proved that the asymptotic distribution of the square root of the estimated PRA is Normal with a constant variance. Statistical tests for hypotheses concerning the independence of two point processes and the characterization of a Poisson process are proposed. Furthermore, approximate 95% pointwise confidence interval can be obtained for the estimated PRA. These results can be applied on stochastic systems involving as input and output stationary point processes. An illustrative example from the framework of neurophysiology is presented.

A Monte Carlo Method Used for the Identification of the Muscle Spindle

In this chapter we describe the behavior of the muscle spindle by using a logistic regression model. The system receives input from a motoneuron and fires through the Ia sensory axon that transfers the information to the spinal cord and from there to the brain. Three functions which are of special interest are included in the model: the threshold, the recovery and the summation functions. The most favorable method of estimating the parameters of the muscle spindle is the maximum likelihood approach. However, there are cases when this approach fails to converge because some of the model’s parameters are considered to be perfect predictors. In this case, the exact likelihood can be used, which succeeds in finding the estimates and the exact confidence intervals for the unknown parameters. This method has a main drawback: it is computationally very demanding, especially with large data sets. A good alternative in this case is a specific application of the Monte Carlo technique.

Editorial: Current Trends of Insect Physiology and Population Dynamics: Modeling Insect Phenology, Demography, and Circadian Rhythms in Variable Environments

Citation: Damos PT, Stoeckli SC and Rigas A (2018) Editorial: Current Trends of Insect Physiology and Population Dynamics: Modeling Insect Phenology, Demography, and Circadian Rhythms in Variable Environments. Front. Physiol. 9:336. doi: 10.3389/fphys.2018.00336 Editorial: Current Trends of Insect Physiology and Population Dynamics: Modeling Insect Phenology, Demography, and Circadian Rhythms in Variable Environments

A Bayesian Logistic Regression approach in Asthma Persistence Prediction

Background : A number of models based on clinical parameters have been used for the prediction of asthma persistence in children. The number and significance of factors that are used in a proposed model play a cardinal role in prediction accuracy. Different models may lead to different significant variables. In addition, the accuracy of a model in medicine is really important since an accurate prediction of illness persistence may improve prevention and treatment intervention for the children at risk. Methods : Data from 147 asthmatic children were analyzed by a new method for predicting asthma outcome using Principal Component Analysis (PCA) in combination with a Bayesian logistic regression approach implemented by the Markov Chain Monte Carlo (MCMC). The use of PCA is required due to multicollinearity among the explanatory variables. Results : This method using the most appropriate models seems to predict asthma with an accuracy of 84.076% and 86.3673%, a Sensitivity of 84.96% and 87.25% and a Specificity of 83.22% and 85.52%, respectively. Conclusion : Our approach predicts asthma with high accuracy, gives steadier results in terms of positive and negative patients and provides better information about the influence of each factor (demographic, symptoms etc.) in asthma prediction.