Vili Podgorelec

Decision Trees: An Overview and Their Use in Medicine

In medical decision making (classification, diagnosing, etc.) there are many situations where decision must be made effectively and reliably. Conceptual simple decision making models with the possibility of automatic learning are the most appropriate for performing such tasks. Decision trees are a reliable and effective decision making technique that provide high classification accuracy with a simple representation of gathered knowledge and they have been used in different areas of medical decision making. In the paper we present the basic characteristics of decision trees and the successful alternatives to the traditional induction approach with the emphasis on existing and possible future applications in medicine.

A survey of genetic algorithms for solving multi depot vehicle routing problem

We reviewed the use of genetic algorithms on the MDVRP (multi depot vehicle routing problem).Survey was made on every operator and setting of genetic algorithm for this problem.We tested different genetic operators and compared the results.We compared the genetic algorithms to other metaheuristic algorithms on MDVRP based on the results on standard benchmarks. This article presents a survey of genetic algorithms that are designed for solving multi depot vehicle routing problem. In this context, most of the articles focus on different genetic approaches, methods and operators, commonly used in practical applications to solve this well-known and researched problem. Besides providing an up-to-date overview of the research in the field, the results of a thorough experiment are presented and discussed, which evaluated the efficiency of different existing genetic methods on standard benchmark problems in detail. In this manner, the insights into strengths and weaknesses of specific methods, operators and settings are presented, which should help researchers and practitioners to optimize their solutions in further studies done with the similar type of the problem in mind. Finally, genetic algorithm based solutions are compared with other existing approaches, both exact and heuristic, for solving this same problem.

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.

Knowledge discovery with classification rules in a cardiovascular dataset

In this paper we study an evolutionary machine learning approach to data mining and knowledge discovery based on the induction of classification rules. A method for automatic rules induction called AREX using evolutionary induction of decision trees and automatic programming is introduced. The proposed algorithm is applied to a cardiovascular dataset consisting of different groups of attributes which should possibly reveal the presence of some specific cardiovascular problems in young patients. A case study is presented that shows the use of AREX for the classification of patients and for discovering possible new medical knowledge from the dataset. The defined knowledge discovery loop comprises a medical experts assessment of induced rules to drive the evolution of rule sets towards more appropriate solutions. The final result is the discovery of a possible new medical knowledge in the field of pediatric cardiology.

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.

Improving mining of medical data by outliers prediction

In the paper a new outlier prediction method is presented that should improve the classification performance when mining the medical data. The method introduces the class confusion score metric that is based on the classification results of a set of classifiers, induced by an evolutionary decision tree induction algorithm. The classification improvement should be achieved by removing the identified outliers from a training set. Our proposition is that a classifier trained by a filtered dataset captures a better, more general knowledge model and should therefore perform better also on unseen cases. The proposed method is applied on the two cardio-vascular datasets and the obtained results are discussed.

Evolutionary induced decision trees for dangerous software modules prediction

Abstract We study the possibility of constructing decision trees with evolutionary algorithms in order to increase their predictive accuracy. We present a self-adapting evolutionary algorithm for the induction of decision trees and describe the principle of decision making based on multiple evolutionary induced decision trees—decision forest. The developed model is used as a fault predictive approach to foresee dangerous software modules, which identification can largely enhance the reliability of software.

Medical diagnostic process optimization through the semantic integration of data resources

In this paper we study the optimization of medical diagnostic process from the data access point of view. According to many studies which showed that optimized diagnostic process can considerably improve efficiency in health care industry, we present a new approach to data integration within a diagnostic process. It is our belief that a unified access to data resources throughout the whole diagnostic process considerably improves the efficiency of the process itself. When combining the optimized data access with an existing algorithmic optimization method an optimized process can be achieved that takes into account the quality of a diagnosis, the individual needs of each patient, the associated costs, and the utilization of personnel/equipment. To enable an efficient management of data, we developed a semantic web based system for the integration of data resources within a medical diagnostic process. Then we combined the unified data access with our existing diagnostic process optimization framework that uses machine learning techniques and evolutionary algorithms. The new defined diagnostic process framework is finally used in a case-study for optimizing the diagnosing of the mitral valve prolapse syndrome in a regional hospital department.

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.

Improving design pattern adoption with Ontology-Based Design Pattern Repository

Design patterns are a proven way to build high-quality software. The number of design patterns is rising rapidly, while management and searching facilities seems not to catch up. This is why selecting a suitable design pattern is not always an easy task. This issue is especially clear for less experienced developers. In this paper we present our approach to cope with the presented issue - an experiment prototype of a new design pattern repository, based on semantic web technologies. Since new ontology-based design pattern repository is a work in progress we point out its potentials for improving design pattern adoption.