Smaranda Belciug

A hybrid neural network/genetic algorithm applied to breast cancer detection and recurrence

Genetic algorithms (GAs) and neural networks (NNs) are both inspired by computation in biological systems and many attempts have been made to combine the two methodologies to boost the NNs performance. This paper deals with the evolutionary training of a feedforward NN for both breast cancer detection and recurrence. A multi-layer perceptron (MLP) has been designed for this purpose, using a GA routine to set weights, and a Java implementation of this hybrid model has been made. Four databases concerning cancer detection and recurrence have been used, two databases containing numerical attributes only, one database containing ordinal (categorical) attributes solely and one database with mixed attributes. In comparison to some standard NNs, the performance of this approach using the same databases is shown to be superior. Moreover, this hybrid MLP/GA model is very flexible in terms of providing accurate classification, even with different types of attributes, which is usually found in medical studies.

Intelligent decision-making for liver fibrosis stadialization based on tandem feature selection and evolutionary-driven neural network

Hepatic fibrosis represents the principal pointer to the development of liver diseases. The correct evaluation of its degree, based on both recent non-invasive procedures and machine learning models, is of current major concern. One of the latest medical imaging methodologies for assessing it is the Fibroscan, supported by biochemical and clinical examinations. Since the complex interaction between the Fibroscan stiffness indicator and the biochemical and clinical results is hard to be manually managed towards the liver fibrosis stadialization, well-performing machine learning algorithms have been proposed to support an automatic diagnosis. We propose in this paper a tandem feature selection mechanism and evolutionary-driven neural network as a computer-based support for liver fibrosis stadialization in chronic hepatitis C. A synergetic system, based on both specific statistical tools and the sensitivity analysis provided by neural networks is used for reducing the dimension of the database from twenty-five to just six attributes. An evolutionary-trained neural network is developed afterwards for the classification of the liver fibrosis stages. The tandem approach is direct and simple, resulting from embedding the feature selection system into the method structure, in order to dynamically concentrate the search only on the most relevant attributes. Experimental results and a thorough statistical analysis clearly demonstrated the efficiency of the proposed intelligent system in comparison with other machine learning techniques reported in literature.

Competitive/collaborative neural computing system for medical diagnosis in pancreatic cancer detection

: The use of computer technology to support medical decisions is now widespread and pervasive across a broad range of medical areas. Accordingly, computer-aided diagnosis has become an increasingly important area for intelligent computational systems. This paper describes a competitive/collaborative neural computing system designed to support the medical decision process using medical imaging databases. A concrete example concerning an application to support the differential diagnosis of chronic pancreatitis and pancreatic cancer is also provided.

Error-correction learning for artificial neural networks using the Bayesian paradigm. Application to automated medical diagnosis

Automated medical diagnosis models are now ubiquitous, and research for developing new ones is constantly growing. They play an important role in medical decision-making, helping physicians to provide a fast and accurate diagnosis. Due to their adaptive learning and nonlinear mapping properties, the artificial neural networks are widely used to support the human decision capabilities, avoiding variability in practice and errors based on lack of experience. Among the most common learning approaches, one can mention either the classical back-propagation algorithm based on the partial derivatives of the error function with respect to the weights, or the Bayesian learning method based on posterior probability distribution of weights, given training data. This paper proposes a novel training technique gathering together the error-correction learning, the posterior probability distribution of weights given the error function, and the Goodman-Kruskal Gamma rank correlation to assembly them in a Bayesian learning strategy. This study had two main purposes; firstly, to develop anovel learning technique based on both the Bayesian paradigm and the error back-propagation, and secondly,to assess its effectiveness. The proposed model performance is compared with those obtained by traditional machine learning algorithms using real-life breast and lung cancer, diabetes, and heart attack medical databases. Overall, the statistical comparison results indicate that thenovellearning approach outperforms the conventional techniques in almost all respects.

Boosting backpropagation algorithm by stimulus-sampling

Neural networks (NNs), in general, and multi-layer perceptron (MLP), in particular, represent one of the most efficient classifiers among the machine learning (ML) algorithms. Inspired by the stimulus-sampling paradigm, it is plausible to assume that the association of stimuli with the neurons in the output layer of a MLP can increase its performance. The stimulus-sampling process is assumed memoryless (Markovian), in the sense that the choice of a particular stimulus at a certain step, conditioned by the whole prior evolution of the learning process, depends only on the networks answer at the previous step. This paper proposes a novel learning technique, by enhancing the standard backpropagation algorithm performance with the aid of a stimulus-sampling procedure applied to the output neurons. The network uses the observable behavior that varies throughout the training process by stimulating the correct answers through corresponding rewards/penalties assigned to the output neurons. The proposed model has been applied in computer-aided medical diagnosis using five real-life breast cancer, colon cancer, diabetes, thyroid, and fetal heartbeat databases. The statistical comparison to well-established ML algorithms proved beyond doubt its efficiency and robustness.

Patient grouping optimization using a hybrid self-organizing map and Gaussian mixture model for length of stay-based clustering system

Clustering is a major tool in data analysis, dividing objects into different groups, based on unsupervised training procedures. Clustering algorithms attempt to group a set of objects into well-defined subgroups, based on some similarity between them. The results of the clustering process may not be confirmed by our knowledge of the data. The self-organizing map (SOM) neural network is an excellent tool in recognizing clusters of data, relating similar classes to each other in an unsupervised manner. Basically, SOM is used when the training dataset contains cases featuring input variables without the associated outputs. SOM can also be used for classification when output classes are immediately available; the advantage in this case is its ability to highlight similarities between classes, thus assessing different previous classification approaches. This paper explores the above ability of SOM to validate length of stay-based (LOS) clustering results that obtained using Gaussian mixture modeling (GMM) approach, by comparing the classification accuracy (percentage of samples correctly classified) of different results. The idea behind this attempt is the following: in the first step, each GMM approach provides its own scheme of grouping LOS, and different classes are thus recognized and labeled. In this step, we have considered GMM with different LOS intervals. In the second step, SOM will first learn to recognize clusters of data and, secondly, will compare its clusters map with the previous labeled clusters provided by GMM. To conclude, a closer similarity between previous clustering schemes and SOM clusters map, will results in a better accuracy for clustering LOS data. Ultimately, by comparing different GMM component models, the SOM application will lead to an optimal number of patient groups. An application to a surgical dataset showed the effectiveness of this methodology in determining the LOS intervals.

Machine Learning Solutions in Computer-Aided Medical Diagnosis

The explosive growth of medical databases and the widespread development of high performance machine learning (ML) algorithms led to the search for efficient computer-aided medical diagnosis (CAMD) techniques. Automated medical diagnosis can be achieved by building a model of a certain disease under surveillance and comparing it with the real time physiological measurements taken from the patient. If this practice is carried out on a regular basis, potential risky medical conditions can be detected at an early stage, thus making the process of fighting the disease much easier. With CAMD, physicians can trustfully use the “second opinion” of the ‘digital assistant’ and make the final optimum decision. The recent development of intelligent technologies, designed to enhance the process of differential diagnosis by using medical databases, significantly enables the decision-making process of health professionals. Up-to-date online medical databases can now be used to support clinical decision-making, offering direct access to medical evidence. In this paper, we provide an overview on selected ML algorithms that can be applied in CAMD, focusing on the enhancement of neural networks (NNs) by hybridization, partially connectivity, and alternative learning paradigms. Particularly, we emphasize the benefits of using such effective algorithms in breast cancer detection and recurrence, colon cancer, lung cancer, liver fibrosis stadialization, heart attack, and diabetes. Generally, the aim is to provide a theme for discussions on ML-based methods applied to medicine.

Intelligent Decision Support Systems in Automated Medical Diagnosis

The Intelligent Decision Support Systems (IDSSs) represent an interdisciplinary research domain bringing together Artificial Intelligence/Machine Learning (AI/ML), Decision Science (DS), and Information Systems (IS). IDSS refers to the use of AI/ML techniques in decision support systems. In this context, it should be emphasized the special role of statistical learning (SL) in the process of training algorithms from data. The purpose of this chapter is to provide a short review of some of the state-of-the-art AI/ML algorithms, seen as intelligent tools used in the medical decision-making, along with some important applications in the automated medical diagnosis of some major chronic diseases (MCDs). In addition, we aim to present an interesting approach to develop novel IDSS inspired by the evolutionary paradigm.

Regression-Based Approach For Feature Selection In Classification Issues. Application To Breast Cancer Detection And Recurrence

Abstract Feature selection is considered a key factor in classifications/decision problems. It is currently used in designing intelligent decision systems to choose the best features which allow the best performance. This paper proposes a regression-based approach to select the most important predictors to significantly increase the classification performance. Application to breast cancer detection and recurrence using publically available datasets proved the efficiency of this technique.

Genetic Algorithms for Breast Cancer Diagnostics

Breast cancer is the most common cancer in women worldwide, representing about 12% of all new cancer cases and 25% of all cancers in women (2012). The widespread development of high-performance machine learning (ML) algorithms, with focus put on genetic algorithms (GAs) led to the search of efficient tools for breast cancer diagnostics. There is no sure way to prevent breast cancer, but its early detection using the GAs support might lower the risk of complications. The automatic breast cancer early detection can be achieved by building a reliable GA model, thus making the process of fighting the disease much easier. With such an approach, researchers and physicians can trustfully use the “digital second opinion” and make the final optimum decision. In this review, we provide an overview on some GA algorithms that can be applied in breast cancer diagnostics, thus emphasizing the benefits of using such effective algorithms as a physicians “intelligent digital assistant.”