Retraction Note: Classification of heart disease using adaptive Harris hawk optimization-based clustering algorithm and enhanced deep genetic algorithm

This paper proposed the heart disease diagnosis system using nonlinear ARX (NARX) model. The system uses neural network for model estimation and classification of Normal and several heart diseases based on heart sounds. In classification, a spectrogram was applied to the modeled heart sounds for features extraction and selection. The features were fed to the FFNN and trained using Resilient Backpropagation (RPROP) algorithm. With optimized learning parameter of 0.07, the network gave best performance at 32-220-6. The accuracy of the network when validated with the diagnostic test was above 97% which suggests that the network performed well and was doing as gold standard. The classification of heart diseases was further improved to 100% when overall testing was performed.

Classification and prediction of heart disease is a significant problem to realize medical treatment and life protection. In this paper, persistent homology is involved to analyze electrocardiograms and a novel heart disease classification method is proposed. Each electrocardiogram becomes a point cloud by sliding windows and fast Fourier transform embedding. The obtained point cloud reveals periodicity and stability characteristics of electrocardiograms. By persistent homology, three features including normalized persistent entropy, maximum life of time and maximum life of Betty number are extracted. These features show the structural differences between different types of electrocardiograms and display encouraging potentiality in classification of heart disease.

Results in heart disease classification that are inaccurate and have low accuracy can endanger the patient's life. Some parameters in the algorithm model also influence classification. This study compares the Decision Tree and Random Forest algorithms for heart disease. The influence of maximum depth on heart disease classification also has significant implications. If the maximum depth is not set correctly, the classification results can be inaccurate and lead to incorrect diagnoses. This study uses five data split schemes, namely 60%: 40%, 70%: 30%, 75%: 25%, 80%: 20%, 90%: 10% and tested with different max depth parameters, namely max depth = 3, 4, 5, 6, and 7. This research produces the best accuracy using the 90%:10% scheme and max depth = 7 with the best accuracy result using the Random Forest algorithm of 99.29% while the Decision Tree algorithm is 98.05%. Then the precision and recall value of the Random Forest algorithm is 99% while the Decision Tree is 98%. The results of computation time using Decision Tree are faster than using Random Forest with a computation time for training data of 0.0075 s, while the testing data are 0.009 s. In future research, research can be conducted on the effect of other parameters by testing using several data sets.

The course of most cardiovascular diseases in males and females has specific features. Objective. To study the gender-specific features of the modified classification of ischemic heart diseases (IHDs) when assessing the risk of an adverse cardiovascular outcome. Materials and methods. A survey to detect angina pectoris (AP) and myocardial infarction (MI) and a unified electrocardiogram analysis using the Minnesota code were conducted on 13.745 participants in the ESSAY-RF study (40.7% males) aged 25-64 from representative samples of the population of nine regions of the Russian Federation. Over 9.5 years of prospective follow-up, a composite endpoint (CEP; cardiovascular death and/or non-fatal MI or stroke) was identified in 611 (4.4%) subjects, of which 364 (6.5%) were males and 247 (3.0%) were females. Prognostic value was determined by Kaplan—Meier methods and proportional Cox risk-adjusted in sex groups for age and region, in the population for age, region, and sex (model 1), and additionally for smoking and cardiometabolic parameters (continuous and discrete, models 2 and 3). Results. The hazard ratio (HR) of CEP in the new IHDs classification was higher than in the old one. In the definite IHD group, the HR was 3.57 versus 2.6 in males and 3.58 versus 1.98 in females (model 1). In models 2 and 3 (2/3), relative to model 1, the HR was lower at 3.19/3.16 compared to 3.57 in males and 2.52/2.69 compared to 3.58 in females. According to studies, it is impossible to differentiate the survival of individuals in any of the groups according to the criteria of the old classification of IHD. The new classification showed a significantly lower survival rate in men (especially with definite IHD nosology) than in women. Conclusion. The modified classification of ischemic heart diseases is adequate to assess the HR of the composite endpoint in males and females. Compared to women, men of working age have a higher HR of the composite endpoint and poorer survival.

Cardiovascular Disease (CVD) is one of the primary diseases that causes death every year. An approximation of roughly about 17.5 million people dies due to CVD, signifying about 31% of global deaths. Based on the statistics, every 34 seconds, a person dies due to heart disease. Various classification algorithms have been developed and utilized as classifiers to support doctors who are ineffectually diagnosed with heart disease. The main aim of this work is to improve the performance of the heart disease approach using image processing algorithm. To improve the effectiveness and efficiency of classification performance for heart disease diagnosis, an optimized neural network was proposed based on the feature extraction and selection approach for handling features. The objective of this investigation is to diagnose heart diseases using feature extraction, reduction based classification and image processing methods. The proposed model comprises of two subsets: Feature extraction using gray scale properties and Moth flame optimization (MFO) for effectual feature selection, followed by a classification technique using Generalized Regression Neural Network. The first system includes three stages: (i) Pre-processing of the dataset (ii) feature extraction (iii) performing MFO for efficient selection. In the second method, GRNN is proposed. The heart data set obtained from ACDC Challenge, was utilized for performing the computation. The image obtained from the MRI-scanner is in the NIfTI image format. The pre-process step used in this is to convert the image type from INT16 to uint8 to improve the quality of image viewing and for feature extraction process. In this phase, the texture properties from the pre-processed image are calculated and the value is in the numeric format. These values are the feature attributes of the dataset. The feature attributes of the image are given as input for the moth flame optimization process and output is the feature selected from the optimization process. The whole process is performed on the feature attributes of the image by determining the optimal feature for the classifier by reducing its error rate. The optimal feature from the moth flame optimization is used for training and testing the network. The classifier used in this approach is a single neural network classifier with a regression nature. Due to the regression property, the network is well trained with the feature. The Generalized regression neural network is used for classifying heart diseases. The proposed method achieves the accuracy of 96.23%, sensitivity of 95.41% and specificity of 96.75%. These values are calculated based on the confusion matrix of the classifier. In this, the feature extraction using the gray scale properties plays an important role to determine the feature attributes from the MRI heart images. The Moth flame optimization able to produce an accuracy of 97.23% using GRNN for classification with minimum single attribute mean of the image. It also outperformed the other methods, either the feature extraction based classification or the feature reduction based classification.

Heart disease is a leading cause of death worldwide, and the need for effective predictive systems is a major source of the need to treat affected patients. This study aimed to determine how to improve the accuracy of Random Forest in predicting and classifying heart disease. The experiments performed in this study were designed to select the most optimal parameters using an RF optimization technique using GA. The Genetic Algorithm (GA) is used to optimize RF parameters to predict and classify heart disease. Optimization of the Random Forest parameter using a genetic algorithm is carried out by using the Random Forest parameter as input for the initial population in the Genetic Algorithm. The Random Forest parameter undergoes a series of processes from the Genetic Algorithm: Selection, Crossover Rate, and Mutation Rate. The chromosome that has survived the evolution of the Genetic Algorithm is the best population or best parameter Random Forest. The best parameters are stored in the hall of fame module in the DEAP library and used for the classification process in Random Forest. The optimized RF parameters are max_depth, max_features, n_estimator, min_sample_leaf, and min_sample_leaf. The experimental process performed in RF uses the default parameters, random search, and grid search. Overall, the accuracy obtained for each experiment is the default parameter 82.5%, random search 82%, and grid search 83%. The RF+GA performance is 85.83%; this result is affected by the GA parameters are generations, population, crossover, and mutation. This shows that the Genetic Algorithm can be used to optimize the parameters of Random Forest.

Wireless sensor networks (WSNs) consist of a large number of small, low data rate and inexpensive nodes that communicate in order to sense or control a physical phenomenon. The major difference between the WSN and the traditional wireless network is that sensors are very sensitive to energy consumption. Moreover, the performance of the sensor network applications highly depends on the lifetime of the network and we expect the lifetime of several months to several years. Thus, energy saving is crucial in designing long-lived wireless sensor networks. Many researchers have focused on developing energy efficient cluster based protocols for WSNs, but there has not been much research on event driven WSNs and, their focus is on continuous driven networks. In this paper, we propose a modified algorithm of Low Energy Adaptive Clustering Hierarchy (LEACH) protocol which is a well known energy efficient clustering algorithm for WSNs. Our modified protocol called “Adaptive and Energy Efficient Clustering Algorithm for Event-Driven Application in Wireless Sensor Networks (AEEC)” is aimed at prolonging the lifetime of a sensor network by balancing energy usage of the nodes. AEEC makes the nodes with more residual energy have more chances to be selected as cluster head. Also, we use elector nodes which take the responsibility of collecting energy information of the nearest sensor nodes and selecting the cluster head. We compared the performance of our AEEC algorithm with the LEACH protocol using simulations .

Heart disease is the foremost cause of death. According to WHO, deaths due to heart disease account for 30% of the global deaths. The main challenge that medical practitioners face is the disease not being identified at an early stage as the traditional approaches are quite time-consuming. Machine learning algorithms can help to deal with the emerging challenges in heart disease classification. The proposed work presents a short review about heart disease classification problem, many classification techniques along with future research directions.

Adaptive gravitational clustering algorithm integrated with noise detection

OWING TO a decline in infectious-disease deaths and an apparent increase in some types of chronic diseases, cardiovascular diseases are now the leading cause of death in the United States. Some investigators believe that we are now witnessing an "epidemic" of coronary heart disease (CHD) among the white, male population of the United States and England. Others feel that this apparent increase in CHD results from a greater proportion of persons in the older age groups in the population, from changes in the classification of heart disease, from better diagnosis by physicians, and from more widely accepted usage of the term, "coronary heart disease," in certifying causes of death.¹ Coronary heart disease is relatively rare among white women before the age of menopause. Mortality statistics in the United States for the past 40 years show an increase in cardiovascular-renal disease death rates for white males and a decrease

<p>On-going big data from social networks sites alike Twitter or Facebook has been an entrancing hotspot for investigation by researchers in current decades as a result of various aspects including up-to-date-ness, accessibility and popularity; however anyway there may be a trade off in accuracy. Moreover, clustering of twitter data has caught the attention of researchers. As such, an algorithm which can cluster data within a lesser computational time, especially for data streaming is needed. The presented adaptive clustering and classification algorithm is used for data streaming in Apache spark to overcome the existing problems is processed in two phases. In the first phase, the input pre-processed twitter data is viably clustered utilizing an Improved Fuzzy C-means clustering and the proposed clustering is additionally improved by an Adaptive Particle swarm optimization (PSO) algorithm. Further the clustered data streaming is assessed utilizing spark engine. In the second phase, the input pre-processed Higgs data is classified utilizing the modified support vector machine (MSVM) classifier with grid search optimization. At long last the optimized information is assessed in spark engine and the assessed esteem is utilized to discover an accomplished confusion matrix. The proposed work is utilizing Twitter dataset and Higgs dataset for the data streaming in Apache Spark. The computational examinations exhibit the superiority of presented approach comparing with the existing methods in terms of precision, recall, F-score, convergence, ROC curve and accuracy.</p>

SummaryHeart disease detection through early‐stage syndrome remains as a main confront in present world situation. If it is not detected appropriate time, then this turns out to be the major cause of death. Several existing heart disease detection techniques are developed with lower detection performance and therefore it is very significant to introduce a novel heart disease detection model that poses the potential to detect heart disease from input data. A novel detection approach named, social water cycle algorithm‐based deep residual network (SWCA‐based DRN) is proposed for classification of heart disease. The developed SWCA algorithm is a newly designed by the hybridization of social optimization algorithm and water cycle algorithm. Here, an input data is initially preprocessed and the feature fusion procedure is carried out RV coefficient enabled rider optimization algorithm‐based neural network. With the fused feature result, heart disease classification is performed utilizing a DRN classifier where training procedure of DRN is done by proposed optimization algorithm, named SWCA. Furthermore, developed SWCA‐enabled DRN technique outperformed different other present heart disease detection approaches and attained superior performance concerning the performance measures, like testing accuracy, sensitivity, and specificity with highest values of 0.941, 0.954, and 0.925.

A questionnaire method was developed for the study of 2,600 cases of proved acute coronary occlusion. The data included the hour, the day, and the type of effort or the mode of rest at the onset of the episode. The results did not show that occupation, exertion, or state of inactivity played any part in precipitating the occlusion. There was no evidence that one attack of acute coronary occlusion predisposed to a second attack except indirectly because of the underlying atherosclerosis. The author does not believe that coronary occlusion has become epidemic or that it is caused by modern stress and strain; he believes that its apparent incidence has increased because of the increased age of the population, improved diagnostic skill of physicians, and changes in the classification of heart diseases.

A Graph Adaptive Density Peaks Clustering algorithm for automatic centroid selection and effective aggregation

Adaptive density peak clustering based on K-nearest neighbors with aggregating strategy