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EN
Heart disease is the leading cause of death in the world according to the World Health Organization (WHO). Researchers are more interested in using machine learning techniques to help medical staff diagnose or detect heart disease early. In this paper, we propose an efficient medical decision support system based on twin support vector machines (Twin-SVM) for heart disease diagnosing with binary target (i.e. presence or absence of disease). Unlike conventional support vector machines (SVM) that finds only one optimal hyperplane for separating the data points of first class from those of second class, which causes inaccurate decision, Twin-SVM finds two non-parallel hyper-planes so that each one is closer to the first class and is as far from the second class as possible. Our experiments are conducted on real heart disease dataset and many evaluation metrics have been considered to evaluate the performance of the proposed method. Furthermore, a comparison between the proposed method and several well-known classifiers as well as the state-of-the-art methods has been performed. The obtained results proved that our proposed method based on Twin-SVM technique gives promising performances better than the state-of-the-art. This improvement can seriously reduce time, materials, and labor in healthcare services while increasing the final decision accuracy.
EN
Cloud computing is a very popular computing model, which grants a manageable infrastructure for various kinds of functions, like storage of data, application realization and presenting, and delivery of information. The concept is therefore very dynamically advancing in all kinds of organisations, including, in particular, the health care sector. However, effective analysis and extraction of information is a challenging issue that must find adequate solutions as soon as possible, since the medical scenarios are heavily dependent on such computing aspects as data security, computing standards and compliance, governance, and so on. In order to contribute to the resolution of the issues, associated with these aspects, this paper proposes a privacy-preserving algorithm for both data sanitization and restoration processes. Even though a high number of researchers contributed to the enhancement of the restoration process, the joint sanitization and restoration process still faces some problems, such as high cost. To attain better results with a possibly low cost, this paper proposes a hybrid algorithm, referred to as GlowWorm Swarm Employed Bee (GWOSEB) for realization of both data sanitization and data restoration process. The proposed GWOSEB algorithm is compared as to its performance with some of the existing approaches, such as the conventional Glowworm Swarm Optimization (GSO), FireFly (FF), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Genetic Algorithm (GA), and Genetically Modified Glowworm Swarm (GMGW), in terms of analysis involving the best, worst, mean, median and standard deviation values, sanitization and restoration effectiveness, convergence analysis, and sensitivity analysis of the generated optimal key. The comparison shows the supremacy of the developed approach.
EN
Diagnosis, being the first step in medical practice, is very crucial for clinical decision making. This paper investigates state-of-the-art computational intelligence (CI) techniques applied in the field of medical diagnosis and prognosis. The paper presents the performance of these techniques in diagnosing different diseases along with the detailed description of the data used. This paper includes basic as well as hybrid CI techniques that have been used in recent years so as to know the current trends in medical diagnosis domain. The paper presents the merits and demerits of different techniques in general as well as application specific context. This paper discusses some critical issues related to the medical diagnosis and prognosis such as uncertainties in the medical domain, problems in the medical data especially dealing with time-stamped (temporal) data, and knowledge acquisition. Moreover, this paper also discusses the features of good CI techniques in medical diagnosis. Overall, this review provides new insight for future research requirements in the medical diagnosis domain.
4
Content available remote Fuzzy classification of medical data derived from diagnostic devices
EN
The research described in this paper concerns fuzzy classification of medical datasets obtained from diagnostic devices. Experimental studies were performed with use of fuzzy c-means algorithm. It was shown that despite the low accuracy of the results, fuzzy classification reduce the risks associated with the loss of internal relationships in the characteristics of the data, and thus increases the chances of finding the pathological cases, as well as taking preventive actions or therapy.
PL
W ramach niniejszej pracy przeprowadzona została klasyfikacja rozmyta w odniesieniu do medycznych zbiorów danych pozyskanych z urządzeń diagnostycznych. Zastosowana została rozmyta metoda k-średnich. Badania wykazały, że pomimo niskiej dokładności rezultatów, klasyfikacja rozmyta zmniejsza ryzyko związane z utratą wewnętrznych zależności w charakterystyce danych, a tym samym zwiększa szanse na stwierdzenie ryzyka patologii i tym samym szybsze podjęcie działań zapobiegawczych lub terapeutycznych.
EN
The use of ensemble of classifiers for classification of medical data derived from diagnostic devices has been proposed in this research. The experimental studies were carried out on three datasets concerning different medical problems: arrhythmia, breast cancer and coronary artery disease using SPECT images. The comparison of single classification algorithms (kNN- IBk, C4.5 - J48, Naïve Bayes, Random Tree and SMO) with bagging, boosting and majority voting using all single classifiers was performed. Experimental studies have proved that hybrid classifiers outperformed single classification in all cases in terms of accuracy, precision, sensitivity and root squared mean error, regardless of the dataset.
PL
W ramach niniejszej pracy zaproponowane zostało zastosowanie komitetów klasyfikatorów w procesie klasyfikacji danych pochodzących z urządzeń medycznych. Badania eksperymentalne zostały przeprowadzone na trzech zbiorach danych dotyczących różnych problemów medycznych: arytmii, nowotworu piersi oraz choroby wieńcowej. Przeprowadzono porównanie pojedynczych technik klasyfikacji (kNNIBk, C4.5 - J48, Naïve Bayes, Random Tree oraz SMO) z metodami hybrydowymi (bagging, boosting oraz głosowanie większościowe). Badania eksperymentalne wykazały skuteczność klasyfikacji z zastosowaniem komitetów klasyfikatorów – w wszystkich badanych przypadkach rezultaty klasyfikacji hybrydowej były lepsze od wyników najlepszego pojedynczego klasyfikatora biorąc pod uwagę dokładność, precyzję, czułość oraz błąd średniokwadratowy.
PL
Artykuł zawiera przegląd zastosowań nowoczesnej technologii informacyjnej w medycynie i diagnostyce oraz wskazuje trudności na drodze postępującej informatyzacji wyżej wymienionych dziedzin . Do najczęściej stosowanych narzędzi informatycznych w służbie zdrowia należą bazy danych, narzędzia bazujące na algorytmach decyzyjnych i metodach przetwarzania danych. Do najważniejszych przeszkód w stosowaniu w/w metod zalicza się heterogeniczność danych medycznych, ich złożoność i konieczność interpretacji opisów słownych.
UK
Стаття містить огляд інформації про застосування сучасних інформаційних технологій у медицині та діагностиці, а також вказуються труднощі на шляху комп'ютеризації в вищезазначених областях. До найчастіше використовуваних інструментів у галузі охорони здоров'я відносяться бази даних, засоби які опираються на алгоритмах прийняття рішень і методах обробки даних. Найважливішими перешкодами під час застосування вищезазначених методів є багатозначність медичних даних, їх неоднорідність і необхідність інтерпретації вербальних описів.
EN
The article presents an overview of common uses of information technology in medicine and medical diagnostics, also pointing out major obstacles in the process of introducing information technology in the fields above. Information technology tools widely used in medicine include but are not limited to databases, decision algorithms and data processing and mining methods. Major obstacles include heterogeneity of medical data, their complexity and free text descriptions of procedures, diagnoses and interpretations of test results.
EN
In order to recognize early symptoms of melanoma, the lethal cancer of the skin, our group is developing pattern recognition and machine learning tools that may help medical doctors in the melanoma diagnosis. Since in the machine learning approach data from diversified sources are required, in this article we present the Java-based Dermat 1.0 application, a medical software system for management of dermoscopy data and the patients follow-up documentation. The chief objective for this system is to integrate all the management activities (image acquisition, anamneses, medical documentation and annotations) into a selfcontent optimal data base system. Such an integrated approach to digital dermoscopy and management of the patient data between the visits is crucial in comparing the results, storing/retrieving/transmitting dermoscopic images and making a proper and early diagnosis. The Dermat application is distributed among dermatological clinics and private practitioners in accordance to the ‘tool-for-the-data’ model.
EN
This paper presents the usage of logistic regression for predicting the classification of patients into one of the two groups. Our data come from patients who underwent Phadiatop test examinations and patients who underwent colectomy in the University Hospital of Ostrava. As the predictor variables were chosen personal and family anamneses for Phadiatop test and the physiological and operative scores for colectomy. For Phadiatop test, both of these anamneses were divided into four categories according to severity ranked by doctors. Scores for morbidity were based on the POSSUM system. The psychological score comprises 12 factors and the operative score comprises 6. The categorical dependent variable which we want to predict was Phadiatop test (respectively morbidity). The model for Phadiatop test was tested with the use of a medical database of 1027 clients and morbidity was tested upon a medical database of 364 clients. The developed models predict the right results with 75% probability for Phadiatop test and 70% probability for morbidity in surgery.
EN
Classification plays very important role in medical diagnosis. This paper presents fuzzy clustering method dedicated to classification algorithms. It focuses on two additional sub-methods modifying obtained clustering prototypes and leading to final prototypes, which are used for creating the classifier fuzzy if-then rules. The main goal of that work was to examine a performance of the classifier which uses such rules. Commonly used including medical benchmark databases were applied. In order to validate the results, each database was represented by 100 pairs of learning and testing subsets. The obtained classification quality was better in relation to the one of the best classifiers - Lagrangian SVM and suggests that presented clustering with additional sub-methods are appropriate to application to classification algorithms.
10
Content available remote A new method for identifying outlying subsets of data
EN
In various branches of science, e.g. medicine, economics, sociology, it is necessary to identify or detect outlying subsets of data. Suppose that the set of data is partitioned into many relatively small subsets and we have some reason to suspect that one or several of these subsets may be atypical or aberrant. We propose applying a new measure of separability, based on the ideas borrowed from the discriminant analysis. In our paper we define two versions of this measure, both using a jacknife, leave-one-out, estimator of classification error. If a suspected subset is significantly well separated from the main bulk of data, then we regard it as outlying. The usefulness of our algorithm is illustrated on a set of medical data collected in a large survey "Epidemiology of Allergic Diseases in Poland" (ECAP). We also tested our method on artificial data sets and on the classical IRIS data set. For a comparison, we report the results of a homogeneity test of Bartoszyński, Pearl and Lawrence, applied to the same data sets.
11
Content available remote The hospital system of patient treatment management
EN
The effective management of gathered medical data is of utmost importance as accurate diagnosis entirely relies on the data; management cannot improve the data but by all means poor management can deteriorate any conclusion drawn from them.
EN
Dynamically developing visualization techniques based on 3D, CT and MRI scanners require universal image processing algorithms. Image processing procedures include image segmentation process, images taken from different scanners and their visualization. One of the disadvantages of existing algorithms is lack of automated threshold estimating procedures which are crucial for proper image segmentation. The authors focus on modifications of segmentation process, and present universal segmentation algorithm which allows tissues searching without input parameters implementation. The other advantage is possibility of bright object localization not characterized by a significant peak in histogram.
PL
W związku z dynamicznie rozwijającymi się technikami obrazowania danych medycznych pochodzących z różnych typów skanerów 3D, CT, MRI, istnieje zapotrzebowanie na uniwersalne algorytmy przetwarzania obrazu. Przetwarzanie obejmuje przede wszystkim procesy segmentacji obrazów, nakładanie obrazów uzyskanych z różnymi technikami i ich wizualizacja. Autorzy skupili się przede wszystkim na segmentacji obrazów szaro-odcieniowych, reprezentujących obrazy medyczne. Autorzy proponują uniwersalny algorytm segmentacji pozwalający wyszukać tkanki w obrazie bez potrzeby wstępnego określania parametrów. Dodatkową zaletą proponowanego rozwiązania jest możliwość lokalizowania tkanek charakteryzujących się brakiem wyraźnego pliku w histogramie.
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