Информационно-экстремальная классификация карт распределения плотности тока в диагностике ишемии миокарда и некоронарогенных заболеваний сердца

Цель работы — развитие метода распознавания образов, основанного на картах распределения плотности тока. Были отобраны наборы типичных карт в пределах ST-T интервала здоровых добровольцев, больных с ишемической болезнью сердца, пациентов с острым миокардитом и миокардитическим кардиосклерозом, а также карты, зарегистрированные в условиях высокого уровня шума. Эти изображения были классифицированы с помощью информационно-экстремальной интеллектуальной технологии, основанной на методе Кульбака, имеющей размеры в параметрическом пространстве Хемминга. При классификации изображений, входящих в учебную выборку, были получены безошибочные результаты. Была достигнута чувствительность и точность 93 % и 87 % соответственно.Мета роботи — розвиток методу розпізнавання образів, що заснований на картах розподілу густини струму. Було відібрано набори типових карт у межах ST-T інтервала здорових добровольців, хворих на ішемічну хворобу серця (ІХС), пацієнтів із гострим міокардитом і міокардитичним кардіосклерозом, а також карти, зареєстровані в умовах високого рівня шуму. Ці зображення було класифіковано за допомогою інформаційно-екстремальної технології, в основі якої лежить метод Кульбака, що має розміри в параметричному просторі Хемминга. При класифікації зображень, які входять в навчальну вибірку, було отримано безпомилкові результати. Екзаменаційна група містила 203 здорових добровольця і 256 хворих ІХС. Було досягнуто чутливість і точність 93 % і 87 % відповідно.The aim of work is to develop a method of pattern recognition for classification of the current density distribution maps. Sets of typical current density distribution maps for healthy volunteers, patients with coronary artery disease, patients with acute myocarditis and myocardial cardio sclerosis as well as the maps registered under conditions of high noise level were selected within the ST-T interval. These images, representing a training sample, method having sizes in Hamming parametric space. Unmistakable results were obtained for the classification of current density maps, included in the training sample. Then verification of developed classification system was performed. Examination sample was consisted of 203 healthy volunteers and 256 patients with coronary artery disease. Sensitivity and specificity of 93 % and 87 % respectively were reached in this group

Review on Heart Disease Prediction System using Data Mining Techniques

Data mining is the computer based process of analyzing enormous sets of data and then extracting the meaning of the data. Data mining tools predict future trends, allowing business to make proactive, knowledge-driven decisions. Data mining tools can answer business questions that traditionally taken much time consuming to resolve. The huge amounts of data generated for prediction of heart disease are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision making. By using data mining techniques it takes less time for the prediction of the disease with more accuracy. In this paper we survey different papers in which one or more algorithms of data mining used for the prediction of heart disease. Result from using neural networks is nearly 100% in one paper [10] and in [6]. So that the prediction by using data mining algorithm given efficient results. Applying data mining techniques to heart disease treatment data can provide as reliable performance as that achieved in diagnosing heart disease

Data mining of magnetocardiograms for prediction of ischemic heart disease

Ischemic Heart Disease (IHD) is a major cause of death. Early and accurate detection of IHD along with rapid diagnosis are important for reducing the mortality rate. Magnetocardiogram (MCG) is a tool for detecting electro-physiological activity of the myocardium. MCG is a fully non-contact method, which avoids the problems of skin-electrode contact in the Electrocardiogram (ECG) method. However, the interpretation of MCG recordings is time-consuming and requires analysis by an expert. Therefore, we propose the use of machine learning for identification of IHD patients. Back-propagation neural network (BPNN), the Bayesian neural network (BNN), the probabilistic neural network (PNN) and the support vector machine (SVM) were applied to develop classification models for identifying IHD patients. MCG data was acquired by sequential measurement, above the torso, of the magnetic field emitted by the myocardium using a J-T interval of 125 cases. The training and validation data of 74 cases employed 10-fold cross-validation methods to optimize support vector machine and neural network parameters. The predictive performance was assessed on the testing data of 51 cases using the following metrics: accuracy, sensitivity, and specificity and area under the receiver operating characteristic (ROC) curve. The results demonstrated that both BPNN and BNN displayed the highest and the same level of accuracy at 78.43 %. Furthermore, the decision threshold and the area under the ROC curve was -0.2774 and 0.9059, respectively, for BPNN and 0.0470 and 0.8495, respectively, for BNN. This indicated that BPNN was the best classification model, BNN was the best performing model with sensitivity of 96.65 %, and SVM employing the radial basis function kernel displayed the highest specificity of 86.36 %