Stavroula G. Mougiakakou

A multifactorial analysis of obesity as CVD risk factor: use of neural network based methods in a nutrigenetics context.

BackgroundObesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI ≤ 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm.ResultsPDM-ANN and GA-ANN were comparatively assessed in terms of their ability to identify the most important factors among the initial 63 variables describing genetic variations, nutrition and gender, able to classify a subject into one of the BMI related classes: normal and overweight. The methods were designed and evaluated using appropriate training and testing sets provided by 3-fold Cross Validation (3-CV) resampling. Classification accuracy, sensitivity, specificity and area under receiver operating characteristics curve were utilized to evaluate the resulted predictive ANN models. The most parsimonious set of factors was obtained by the GA-ANN method and included gender, six genetic variations and 18 nutrition-related variables. The corresponding predictive model was characterized by a mean accuracy equal of 61.46% in the 3-CV testing sets.ConclusionsThe ANN based methods revealed factors that interactively contribute to obesity trait and provided predictive models with a promising generalization ability. In general, results showed that ANNs and their hybrids can provide useful tools for the study of complex traits in the context of nutrigenetics.

An Insulin Infusion Advisory System for Type 1 Diabetes Patients based on Non-Linear Model Predictive Control Methods

In this paper, an Insulin Infusion Advisory System (IIAS) for Type 1 diabetes patients, which use insulin pumps for the Continuous Subcutaneous Insulin Infusion (CSII) is presented. The purpose of the system is to estimate the appropriate insulin infusion rates. The system is based on a Non-Linear Model Predictive Controller (NMPQ which uses a hybrid model. The model comprises a Compartmental Model (CM), which simulates the absorption of the glucose to the blood due to meal intakes, and a Neural Network (NN), which simulates the glucose-insulin kinetics. The NN is a Recurrent NN (RNN) trained with the Real Time Recurrent Learning (RTRL) algorithm. The output of the model consists of short term glucose predictions and provides input to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. For the development and the evaluation of the HAS, data generated from a Mathematical Model (MM) of a Type 1 diabetes patient have been used. The proposed control strategy is evaluated at multiple meal disturbances, various noise levels and additional time delays. The results indicate that the implemented HAS is capable of handling multiple meals, which correspond to realistic meal profiles, large noise levels and time delays.

DIAGNOSIS: A Telematics Enabled System for Medical Image Archiving, Management and Diagnosis Assistance

In this paper, a modular system for medical image archiving, management, diagnosis support, and telematic cooper- ation is presented. The system provides digital imaging and com- munications in medicine (DICOM)-compatible tools for digital image processing and database management of medical images. The software features algorithms for preprocessing, manual or semi-automatic segmentation, automatic calculation of geomet- rical/size characteristics, and 3-D visualization of organs or se- lected regions of interest. Additionally, the system incorporates a database where patient data and information can be stored and retrieved. Access to the database is only permitted to authorized users. The user-friendly interface makes the software handy and accessible to clinicians, whereas the telematic components allow collaboration with remote experts. The pilot system incorporates a computer-aided diagnosis module aiming at providing support in the diagnosis of focal liver lesions from computed tomography images.

Analysis of postprandial lipemia as a Cardiovascular Disease risk factor using genetic and clinical information: An Artificial Neural Network perspective

Clinical studies indicate that exaggerated postprandial lipemia is linked to the progression of atherosclerosis, leading cause of Cardiovascular Diseases (CVD). CVD is a multi-factorial disease with complex etiology and according to the literature postprandial Triglycerides (TG) can be used as an independent CVD risk factor. Aim of the current study is to construct an Artificial Neural Network (ANN) based system for the identification of the most important gene-gene and/or gene-environmental interactions that contribute to a fast or slow postprandial metabolism of TG in blood and consequently to investigate the causality of postprandial TG response. The design and development of the system is based on a dataset of 213 subjects who underwent a two meals fatty prandial protocol. For each of the subjects a total of 30 input variables corresponding to genetic variations, sex, age and fasting levels of clinical measurements were known. Those variables provide input to the system, which is based on the combined use of Parameter Decreasing Method (PDM) and an ANN. The system was able to identify the ten (10) most informative variables and achieve a mean accuracy equal to 85.21%.

Computer Aided Diagnosis of CT Focal Liver Lesions based on Texture Features, Feature Selection and Ensembles of Classifiers

A computer aided diagnosis system aiming to classify liver tissue from computed tomography images is presented. For each region of interest five distinct sets of texture features were extracted. Two different ensembles of classifiers were constructed and compared. The first one consists of five Neural Networks (NNs), each using as input either one of the computed texture feature sets or its reduced version after feature selection. The second ensemble of classifiers was generated by combining five different type of primary classifiers, two NNs, and three k-nearest neighbor classifiers. The primary classifiers of the second ensemble used identical input vectors, which resulted from the combination of the five texture feature sets, either directly or after proper feature selection. The decision of each ensemble of classifiers was extracted by applying voting schemes.