Supporting medical decisions with vector decision trees.

The article presents the extension of a common decision tree concept to a multidimensional - vector - decision tree constructed with the help of evolutionary techniques. In contrary to the common decision tree the vector decision tree can make more than just one suggestion per input sample. It has the functionality of many separate decision trees acting on a same set of training data and answering different questions. Vector decision tree is therefore simple in its form, is easy to use and analyse and can express some relationships between decisions not visible before. To explore and test the possibilities of this concept we developed a software tool--DecRain--for building vector decision trees using the ideas of evolutionary computing. Generated vector decision trees showed good results in comparison to classical decision trees. The concept of vector decision trees can be safely and effectively used in any decision making process.

Finding the right decision tree's induction strategy for a hard real world problem.

Decision trees have been already successfully used in medicine, but as in traditional statistics, some hard real world problems can not be solved successfully using the traditional way of induction. In our experiments we tested various methods for building univariate decision trees in order to find the best induction strategy. On a hard real world problem of the Orthopaedic fracture data with 2637 cases, described by 23 attributes and a decision with three possible values, we built decision trees with four classical approaches, one hybrid approach where we combined neural networks and decision trees, and with an evolutionary approach. The results show that all approaches had problems with either accuracy, sensitivity, or decision tree size. The comparison shows that the best compromise in hard real world problem decision trees building is the evolutionary approach.

Towards more optimal medical diagnosing with evolutionary algorithms.

Efficiency in hospital performance is becoming more and more important. Studies showed that diagnosis can considerably reduce the inefficiency, so one of the most important tasks in achieving greater hospital efficiency is to optimize the diagnostic process. For the best of the patient the diagnostic process has to be optimized regarding the number of the examinations and individualized in order to maximize accuracy, sensitivity and specificity. In addition the duration of the diagnostic process has to be minimized and the process has to be performed on the most reliable equipment. The main contribution of our paper is the introduction of the integrated computerized environment DIAPRO enabling the diagnostic process optimization. The DIAPRO is based on a single approach--evolutionary algorithms.

Power of heterogeneous computing as a vehicle for implementing E(3) medical decision support systems.

A computer system of PCs, workstations, minicomputers etc. connected together via a local area network or wide area network represents a large pool of computational power. Our aim is to use this power for the implementation of an E(3) (efficiency, effectiveness, efficacy) medical decision support system, which can be based on different models, the best providing an explanation together with an accurate and reliable response. One of the most viable among models is decision trees, already used for many medical decision-making purposes. In this paper, we present a parallel implementation of a genetic algorithm on a heterogeneous computing system for the induction of decision trees with the application on solving the mitral valve prolapse syndrome. Our approach can be considered as a good choice for different real-world decision making, with respect to the advantages of our model, especially the great computational power.

The art of building decision trees.

Decision support systems that help physicians are becoming a very important part of medical decision making. They are based on different models and the best of them are providing an explanation together with an accurate, reliable, and quick response. One of the most viable among models are decision trees, already successfully used for many medical decision-making purposes. Although effective and reliable, the traditional decision tree construction approach still contains several deficiencies. Therefore we decided to develop and compare several decision support models using four different approaches. We took statistical analysis, a MtDeciT, in our laboratory developed tool for building decision trees with a classical method, the well-known C5.0 tool and a self-adapting evolutionary decision support model that uses evolutionary principles for the induction of decision trees. Several solutions were evolved for the classification of metabolic and respiratory acidosis (MRA). A comparison between developed models and obtained results has shown that our approach can be considered as a good choice for different kinds of real-world medical decision making.

Using intelligent search for finding medical sites.

In this paper we present an intelligent search tool, which we developed in order to automate search and evaluation of websites. We used TFIDF heuristics to determine term frequency and decision trees to evaluate the quality of sites. Training set for the decision tree contained manually evaluated websites. Each website was described by the combination of various attributes, complexity metrics and the evaluation. The intelligent search tool is equipped with a user-friendly interface, which enables people to exploit the tool to its limits with minimum effort, in their quest for information. For testing purposes, we looked for sites with telemedical content. The set of sites, which was the result of using the intelligent search tool, has been evaluated by a group of students.

The influence of class discretization to attribute hierarchy of decision trees.

Decision support systems that help physicians are becoming very important part of medical decision making. They are based on different models and the best of them are providing an explanation together with an accurate, reliable and quick response. One of the most viable among decision-making models is the concept of decision trees, already successfully used for many medical decision making purposes. Although effective and reliable, the traditional decision tree construction approach still contains several deficiencies. Therefore we decided to develop and compare several decision supporting models, each of them built with different discretization of attributes and decision classes. For the construction of decision trees we used MtDeciT, in our laboratory developed tool for building decision trees using the classical induction method. All solutions were evolved for determining the influence of basic properties of child and his/her parents to length of successful breastfeeding. A comparison between developed models and obtained results has shown that the way of discretization obviously plays a great role in the reliable and accurate real-world medical decision making.

Power of heterogeneous computing as a vehicle for implementing E3 medical decision support systems.

A computer system of PCs, workstations, minicomputers etc., connected together via a local area network or wide area network represents a large pool of computational power. Our aim is to use this power for the implementation of E3 (efficiency, effectiveness, efficacy) medical decision support system, which can be based on different models; the best of them are providing an explanation together with an accurate and reliable response. One of the most viable among models are decision trees, already used for many medical decision making purposes. In this paper we would like to present a heterogeneous implementation of genetic algorithm for the induction of decision trees with emphasis on solving the mitral valve prolapse syndrome.

Genetic algorithm based system for patient scheduling in highly constrained situations.

In medicine and health care there are a lot of situations when patients have to be scheduled on different devices and/or with different physicians or therapists. It may concern preventive examinations, laboratory tests or convalescent therapies, therefore we are always looking for an optimal schedule that would result in finishing all the activities scheduled as soon as possible, with the least patient waiting time and maximum device utilization. Since patient scheduling is a highly complex problem, it is impossible to make a qualitative schedule by hand or even with exact heuristic methods. Therefore we developed a powerful automated scheduling method for highly constrained situations based on genetic algorithms and machine learning. In this paper we present the method, together with the whole process of schedule generation, the important parameters to direct the evolution and how the algorithm is guaranteed to produce only feasible solutions, not breaking any of the required constraints. We applied the described method to a problem of scheduling patients with different therapy needs to a limited number of therapeutic devices, but the algorithm can be easily modified for use in similar situations. The results are quite encouraging and since all the solutions are feasible, the method can be easily incorporated into an interactive user interface, which can be of major importance when scheduling patients, and human resources in general, is considered.