Classification of Heart Sounds Associated With Murmur For Diagnosis of Cardiac Valve Disorders

Background: Now a day, cardiovascular diseases have been a major cause of death in the world. The heart sound is still the primary tool used for screening and diagnosing many pathological conditions of the human heart. The abnormality in the heart sounds starts appearing much earlier than the symptoms of the disease. In this study, the Phonocardiography signal has been studied and classified into three classes, namely normal signal, murmur signal and extra sound signal. A total of 15 features from different domains have been extracted and then reduced to 7 features. The features have been selected on the basis of correlation based feature selection technique. The selected features are used to classify the signal into the predefined classes using multi- class SVM classifier. The performance of the proposed denoising algorithm is evaluated using the signal to noise ratio, percentage root means square difference, and root mean square error. For this work a publically available database for researchers, Partnership Among South Carolina Academic Libraries (PASCAL) and MATLAB 2018a was used to develop the proposed algorithm.Results: Our experimental result shows that the 4th level of decomposition for the Db10 wavelets shows the highest SNR values when using the soft and hard thresholding. The overall accuracy, Sensitivity and Specificity of the developed algorithm is 97.96%, 97.92 % and of 98.0% respectively.Conclusion: even if the proposed algorithm is useful for murmur detection mainly valve-related diseases and the efficiency of the proposed study is increased, future work will intend to generalize the algorithm by using hybrid classifiers on a larger dataset. Since all experiments used the PASCAL datasets, additional experiments will be needed using new datasets to be implemented using the latest mobile phones which can work as an electronic stethoscope or phonocardiogram. In addition, the case of continuous murmur and types of murmur has been included for classification in further studies.

Machine Learning Classifiers and Along with TPOT Classifier (Automl) to Predict the Readmission Patterns of Diabetic Patients

Diabetes is seen as a common problem in the present running world. And till date 470million people globally in 2019, and it might be increased to 676million by the end of 2045.So day to day the diabetic has become a major problem, and due to the current technologies, we can easily predict the readmission of a patient based upon his digenesis. In this paper we are using classification algorithms to solve the problem by early predictions. And we can check it by using multiple hybrid classifiers, whatever the algorithm gives the best accuracy we are considering it as the generic model and it is going to predict the future diabetic patients. And we are considering the diabetic dataset mainly it consists of multiple features based upon the data we will consider as independent and dependent data, and solve the problem. Here, in this paper the algorithms which we are going to use are Logistic Regression(LR), Decision Trees, Random Forest (RF),XGboost,Gaussian-Naïve Bayes, TPOT(automl).Out of them Random Forest gives the best accuracy which is about 95.2%, the accuracy is attained by following pre-processing stage in a good manner, and handled all missing data.

Enhanced optimal trained hybrid classifiers for aging assessment of power transformer insulation oil

Purpose As a result of the deregulations in the power system networks, diverse beneficial operations have been competing to optimize their operational costs and improve the consistency of their electrical infrastructure. Having certain and comprehensive state assessment of the electrical equipment helps the assortment of the suitable maintenance plan. Hence, the insulation condition monitoring and diagnostic techniques for the reliable and economic transformers are necessary to accomplish a comprehensive and proficient transformer condition assessment. Design/methodology/approach The main intent of this paper is to develop a new prediction model for the aging assessment of power transformer insulation oil. The data pertaining to power transformer insulation oil have been already collected using 20 working power transformers of 16-20 MVA operated at various substations in Punjab, India. It includes various parameters associated with the transformer such as breakdown voltage, moisture, resistivity, tan δ, interfacial tension and flashpoint. These data are given as input for predicting the age of the insulation oil. The proposed aging assessment model deploys a hybrid classifier model by merging the neural network (NN) and deep belief network (DBN). As the main contribution of this paper, the training algorithm of both NN and DBN is replaced by the modified lion algorithm (LA) named as a randomly modified lion algorithm (RM-LA) to reduce the error difference between the predicted and actual outcomes. Finally, the comparative analysis of different prediction models with respect to error measures proves the efficiency of the proposed model. Findings For the Transformer 2, root mean square error (RMSE) of the developed RM-LA-NN + DBN was 83.2, 92.5, 40.4, 57.4, 93.9 and 72 per cent improved than NN + DBN, PSO, FF, CSA, PS-CSA and LA-NN + DBN, respectively. Moreover, the RMSE of the suggested RM-LA-NN + DBN was 97.4 per cent superior to DBN + NN, 96.9 per cent superior to PSO, 81.4 per cent superior to FF, 93.2 per cent superior to CSA, 49.6 per cent superior to PS-CSA and 36.6 per cent superior to LA-based NN + DBN, respectively, for the Transformer 13. Originality/value This paper presents a new model for the aging assessment of transformer insulation oil using RM-LA-based DBN + NN. This is the first work uses RM-LA-based optimization for aging assessment in power transformation insulation oil.