Smaranda Belciug

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This paper aims to assess the effectiveness of three different clustering algorithms, used to detect breast cancer recurrent events. The performance of a classical k-means algorithm is compared with a much more sophisticated Self-Organizing Map (SOM-Kohonen network) and a cluster network, closely related to both k-means and SOM. The three clustering(More)
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(More)
Scarce healthcare resources require carefully made policies ensuring optimal bed allocation, quality healthcare service, and adequate financial support. This paper proposes a complex analysis of the resource allocation in a hospital department by integrating in the same framework a queuing system, a compartmental model, and an evolutionary-based(More)
The purpose of this paper is twofold: first, to propose an evolutionary-based method for building a decision model and, second, to assess and validate the model's performance using five different real-world medical datasets (breast cancer and liver fibrosis) by comparing it with state-of-the-art machine learning techniques. The evolutionary-inspired(More)
The fully connected feed-forward neural networks are commonly used in almost all neural networks applications, since such architecture provides the best generalisation power. However, they need large computing resources and have low speed when they are applied to large databases. The aim of this paper is to assess the effectiveness of an alternative(More)
AIM To analyze whether computer-enhanced dynamic analysis of elastography movies is able to better characterize and differentiate between different degrees of liver fibrosis. METHODS The study design was prospective. A total of 132 consecutive patients with chronic liver diseases and healthy volunteers were examined by transabdominal ultrasound(More)
This paper deals with the comparison of the two neural network methods of learning: supervised (classical feedforward neural networks: multi-layer neural networks (MLP), radial basis function (RBF) and probabilistic neural networks (PNN)) and unsupervised (self organizing feature maps (SOFM), or Kohonen map), in order to assess their performances on a(More)