Medical Diagnosis Of Heart Disease Research Articles

An algorithmic multiple attribute decision-making method for heart problem analysis under neutrosophic hypersoft expert set with fuzzy parameterized degree-based setting

A fuzzy parameterized neutrosophic hypersoft expert set (FpNHse-set) is one of the family members of fuzzy parameterized structures and a valuable extension of the neutrosophic soft expert set as well as the neutrosophic hypersoft set. This structure involves a multi-argument approximate function that has the ability to change the sub-characteristic pairs in the form of power set of universe. The main function of this structure is the classification of each character into sub-characteristic valued sets. Due to this prominent property, this mathematical structure is useful for uncertainties and also helps make the decision-making process more adaptable and dependable. By using the algebraic and basic ideas of the FpNHse-sets, a useful strategy, especially for medical diagnosis, known as Sanchez’s method has been used in this study. To see a reformed process for the medical diagnosis of heart disease, a useful combination of FpNHse-set and a modified Sanchez’s method has been made in this context. By using the real data from the Cleveland data set for heart disease, the implementation of the reform process has been made to see its veracity. Finally, a clear comparison of the used study with its existing studies has been made for the purpose of benefits.

Left Ventricle Segmentation in Echocardiography with Transformer.

Left ventricular ejection fraction (LVEF) plays as an essential role in the assessment of cardiac function, providing quantitative data support for the medical diagnosis of heart disease. Robust evaluation of the ejection fraction relies on accurate left ventricular (LV) segmentation of echocardiograms. Because human bias and expensive labor cost exist in manual echocardiographic analysis, computer algorithms of deep-learning have been developed to help human experts in segmentation tasks. Most of the previous work is based on the convolutional neural networks (CNN) structure and has achieved good results. However, the region occupied by the left ventricle is large for echocardiography. Therefore, the limited receptive field of CNN leaves much room for improvement in the effectiveness of LV segmentation. In recent years, Vision Transformer models have demonstrated their effectiveness and universality in traditional semantic segmentation tasks. Inspired by this, we propose two models that use two different pure Transformers as the basic framework for LV segmentation in echocardiography: one combines Swin Transformer and K-Net, and the other uses Segformer. We evaluate these two models on the EchoNet-Dynamic dataset of LV segmentation and compare the quantitative metrics with other models for LV segmentation. The experimental results show that the mean Dice similarity of the two models scores are 92.92% and 92.79%, respectively, which outperform most of the previous mainstream CNN models. In addition, we found that for some samples that were not easily segmented, whereas both our models successfully recognized the valve region and separated left ventricle and left atrium, the CNN model segmented them together as a single part. Therefore, it becomes possible for us to obtain accurate segmentation results through simple post-processing, by filtering out the parts with the largest circumference or pixel square. These promising results prove the effectiveness of the two models and reveal the potential of Transformer structure in echocardiographic segmentation.

A movable unshielded magnetocardiography system.

Magnetocardiography (MCG), which uses high-sensitivity magnetometers to record magnetic field signals generated by electrical activity in the heart, is a noninvasive method for evaluating heart diseases such as arrhythmia and ischemia. The MCG measurements usually require the participant keeping still in a magnetically shielded room due to the immovable sensor and noisy external environments. These requirements limit MCG applications, such as exercise MCG tests and long-term MCG observations, which are useful for early detections of heart diseases. Here, we introduce a movable MCG system that can clearly record MCG signals of freely behaving participants in an unshielded environment. On the basis of optically pumped magnetometers with a sensitivity of 140 fT/Hz1/2, we successfully demonstrated the resting MCG and the exercise MCG tests. Our method is promising to realize a practical movable multichannel unshielded MCG system that nearly sets no limits to participants and brings another kind of insight into the medical diagnosis of heart disease.

Machine learning-based approach to the diagnosis of cardiovascular vascular disease using a combined dataset

Nowadays, one of the most important illnesses is a heart disease which causes most patients dead. The medical diagnosis of heart disease is quite difficult. This diagnosis is a challenging process that requires accuracy and efficiency. The chance of death will be decreased with early heart disease detection. Because cardiac problems are now a fairly frequent ailment, predicting heart disease has become one of the most difficult medical jobs in recent years. Researchers looked at a variety of closely related traits to discover the most reliable predictors of these conditions. In this study, Machine Learning (ML) techniques are used to identify the presence of cardiac abnormalities. The proposed method predicts the chances of heart disease and classifies patient's risk level by using different ML algorithm techniques such as Decision Tree (DT), Ada-Boost Classifier (AB), Extra trees Classifier (ET), Support vector Machine (SVM), Gradient boost, MLP, extreme gradient boost (XGB), Random Forest (RF), KNN, and LR. Three different datasets are combined to train and test the proposed system. The experimental results show that, when compared to other ML algorithms, the Decision Tree algorithm has the highest accuracy, at 99.16%.

Research on Application of Data Mining Algorithm in Cardiac Medical Diagnosis System.

Heart disease is a very common high-incidence disease. Due to the wide variety of pathology of heart disease, how to improve the medical diagnosis of heart disease and carry out earlier intervention and treatment is a problem that needs to be solved urgently. The paper adds the decision tree algorithm and its comparison and proposes an optimized classification algorithm Co-SVM. Based on the establishment of a heart disease diagnosis classifier based on data mining algorithms, it is aimed at exploring which of these four algorithms is more suitable for heart disease diagnosis problems and optimizing them. A brief description of the cause, influencing factors, and acquired data of heart disease can be seen from the accuracy and scientificity of the data, which further enhances the authenticity and reliability of the clinical diagnosis model of heart disease. At the same time, the ultrasound diagnosis technology of heart disease is introduced, and the important role of ultrasound diagnosis technology in the medical diagnosis of heart disease is discussed. This thesis uses the heart disease clinical data set to establish a heart disease diagnosis classifier based on the decision tree algorithm, neural network algorithm, support vector machine algorithm, and Co-SVM algorithm. Through experimental comparison and analysis, the optimal classification is selected according to the obtained results. The algorithm is Co-SVM algorithm. The experimental results show that the proposed Co-SVM algorithm has a higher accuracy rate than the other three classic algorithms, and the effectiveness of the Co-SVM algorithm is verified by the evaluation results of multiple algorithms. By applying the Co-SVM algorithm in the medical diagnosis system, it is helpful to assist doctors in making more accurate and precise diagnosis of the condition.

An Integrated Algorithmic MADM Approach for Heart Diseases' Diagnosis Based on Neutrosophic Hypersoft Set with Possibility Degree-Based Setting.

The possibility neutrosophic hypersoft set (pNHs-set) is a generalized version of the possibility neutrosophic soft set (pNs-set). It tackles the limitations of the pNs-set regarding the use of the multi-argument approximate function. This function maps sub-parametric tuples to a power set of the universe. It emphasizes the partitioning of each attribute into its respective attribute-valued set. These features make it a completely new mathematical tool for solving problems dealing with uncertainties. This makes the decision-making process more flexible and reliable. In this study, after characterizing some elementary notions and algebraic operations of the pNHs-set, Sanchez’s method (a classical approach for medical diagnosis) is modified under the pNHs-set environment. A modified algorithm is proposed for the medical diagnosis of heart diseases by integrating the concept of the pNHs-set and the modified Sanchez’s method. The authenticity of the proposed algorithm is evaluated through its implementation in a real-world scenario with real data from the Cleveland data set for heart diseases. The beneficial aspects of the proposed approach are evaluated through a structural comparison with some pertinent existing approaches.

An Intelligent Mining Model for Medical Diagnosis of Heart Disease Based on Rough Set Data Analysis

An Intelligent Mining Model for Medical Diagnosis of Heart Disease Based on Rough Set Data Analysis

Evaluating the quality of evidence for diagnosing ischemic heart disease from verbal autopsy in Indonesia.

BACKGROUNDMortality and cause of death data are fundamental to health policy development. Civil Registration and Vital Statistics systems are the ideal data source, but the system is still under development in Indonesia. A national Sample Registration System (SRS) has provided nationally representative mortality data from 128 sub-districts since 2014. Verbal autopsy (VA) is used in the SRS to obtain causes of death. The quality of VA data must be evaluated as part of the SRS data quality assessment.AIMTo assess the strength of evidence used in the assignment of Ischaemic Heart Disease (IHD) as causes of death from VA.METHODSThe sample frame for this study is the 4,070 deaths that had IHD assigned as the underlying cause in the SRS 2016 database. From these, 400 cases were randomly selected. A data extraction form and data entry template were designed to collect relevant data about IHD from VA questionnaires. A standardised categorisation was designed to assess the strength of evidence used to infer IHD as a cause of death. A pilot test of 50 cases was carried out. IBM SPSS software was used in this study.RESULTSStrong evidence of IHD as a cause of death was assigned based on surgery for coronary heart disease, chest pain and two out of: sudden death, history of heart disease, medical diagnosis of heart disease, or terminal shortness of breath. More than half (53%) of the questionnaires contained strong evidence. For deaths outside health facilities, VA questionnaires for male deaths contained acceptable evidence in significantly higher proportions as compared to those for female deaths. (P < 0.001). Nearly half of all IHD deaths were concentrated in the 50-69 year age group (48.40%), and a further 36.10% were aged 70 years or more. Nearly two-thirds of the deceased were male (58.40%). Smoking behaviour was found in 44.11% of IHD deaths, but this figure was 73.82% among males.CONCLUSIONMore than half of the VA questionnaires from the study sample were found to contain strong evidence to infer IHD as the cause of death. Results from medical records such as electrocardiograms, coronary angiographies, and load tests could have improved the strength of evidence and contributed to IHD cause of death diagnosis.

Self-reported medical diagnosis of heart disease and associated risk factors: National Health Survey.

To analyze the risk factors associated with the self-reported medical diagnosis of heart disease in Brazil. This is a cross-sectional study, analyzing information from 60,202 adult participants of the Brazilian National Health Survey in 2013. Heart disease was defined by self-reported medical diagnosis of heart disease. We analyzed associations between the occurrence of disease and sociodemographic characteristics, health conditions and lifestyle. A hierarchical binary logistic regression model was used. The prevalence of self-reported diagnosis of heart disease in Brazil was 4.2% (confidence interval of 95% [95%CI] 4.0 ‒ 4.3) and was associated with females (odds ratio [OR] = 1.1; 95%CI 1.1 ‒ 1.1), people 65 years old or older (OR = 4.7; 95%CI 3.3 ‒ 5.6), poor or very poor health conditions (OR = 4.1; 95%CI 3.5 ‒ 4.6) and fair health conditions (OR = 2.4; 95%CI 2.2 ‒ 2.7), hypertensive individuals (OR = 2.4; 95%CI 2.2 ‒ 2.7), those with increased cholesterol (OR = 1.6; 95%CI 1.5 ‒ 1.8), overweight individuals (OR = 1.5; 95%CI 1.4 ‒ 1.8) and obese individuals (OR = 2.0; 95%CI 1.7 ‒ 2.2), sedentary behavior (OR = 1.5; 95%CI 1.02 ‒ 2.1), former smokers (OR = 1.4; 95%CI 1.3 ‒ 1.6) or current smokers (OR = 1.2; 95%CI 1.03 ‒ 1.3) and the consumption of fruits and vegetables 5 or more days each week (OR = 1.5; 95%CI 1.1 ‒ 1.5). The importance of knowledge on the prevalence of heart disease and associated risk factors in the present Brazilian epidemiological context must be emphasized because it guides actions to control and prevent cardiovascular diseases, the leading cause of death in Brazil and worldwide.

Early readmission in a high complexity public hospital in cardiology

Objective: to evaluate the early readmission of patients in a high complexity public hospital in cardiology. Methods: this is a descriptive, documental and retrospective study, carried out in a public hospital. Results: in 2012, 729 of the 9,218 hospitalized patients were readmitted, 47.9% of them were readmitted within 30 days after discharge, 61% were men, with a mean age of 57 years old, with the main medical diagnosis of heart disease (heart failure, treatment of acute coronary syndrome, among others) on admission (44.7%) and readmission (45.8%). Most were readmitted for the same medical reason that led to their first hospitalization. Conclusion: considering the profile of the population admitted and readmitted to the institution, it is believed that the rate of readmission is mainly due to the profile of the patients, considering the high prevalence of non-communicable chronic diseases, and coronary artery disease considered unapproachable for percutaneous or surgical when hospitalized.

Heart Disease Diagnosis Utilizing Hybrid Fuzzy Wavelet Neural Network and Teaching Learning Based Optimization Algorithm

Among the various diseases that threaten human life is heart disease. This disease is considered to be one of the leading causes of death in the world. Actually, the medical diagnosis of heart disease is a complex task and must be made in an accurate manner. Therefore, a software has been developed based on advanced computer technologies to assist doctors in the diagnostic process. This paper intends to use the hybrid teaching learning based optimization (TLBO) algorithm and fuzzy wavelet neural network (FWNN) for heart disease diagnosis. The TLBO algorithm is applied to enhance performance of the FWNN. The hybrid TLBO algorithm with FWNN is used to classify the Cleveland heart disease dataset obtained from the University of California at Irvine (UCI) machine learning repository. The performance of the proposed method (TLBO_FWNN) is estimated using K-fold cross validation based on mean square error (MSE), classification accuracy, and the execution time. The experimental results show that TLBO_FWNN has an effective performance for diagnosing heart disease with 90.29% accuracy and superior performance compared to other methods in the literature.

Periodontitis in older Swedish individuals fails to predict mortality

This study aims to assess mortality risk and its association to health aspects in dentate individuals 60 years of age and older. Medical and periodontal data from 870 dentate individuals (age range 60–96) participating in the Swedish National Study on Aging and Care in Blekinge (SNACBlekinge)with survival statistics over 6 years were studied. During 6 years of follow-up, 42/474 of the individuals(8.9 %), who at baseline were between age 60 and 75, and 134/396 individuals of the individuals (33.9 %), who at baseline were ≥75 years, died. Surviving dentate individuals had more teeth (mean 19.3, S.D.±7.9) than those who died (mean 15.9,S.D.±7.3; mean diff 3,3; S.E. mean diff 0.7; 95 % CI 2.0, 4.6;p=0.001). A self-reported history of high blood pressure (F=15.0, p<0.001), heart failure (F=24.5, p<0.001, observed power=0.99), older age (F=34.7, p<0.001), male gender(F=6.3, p<0.01), serum HbA1c with 6.5 % as cutoff level(F=9.3, p=0.002) were factors associated with mortality. A medical diagnosis of heart disease, diabetes, any form of cancer,or periodontitis failed to predict mortality. A self-reported history of angina pectoris, chronic heart failure, elevated serum HbA1c, and few remaining teeth were associated with mortality risk. A professional diagnosis of cardiovascular disease, diabetes, cancer, or periodontitis was not predictive of mortality. Self-health reports are important to observe in the assessment of disease and survival in older individual.

Low Sampling-rate Approach for ECG Signals with Compressed Sensing Theory

Wireless Body Area Networks (WBANs) consist of tiny Biomedical Wireless Sensors (BWSs) and a Gate Way (GW) to connect to the external databases in the hospital and medical centres. The GW could connect the BWSs, to a range of wireless telecommunication networks. These wireless telecommunication networks could be either a mobile phone network, a standard telephone network, a dedicated medical centre or using public Wireless Local Area Networks (WLANs) nodes also known a Wi-Fi system. The electrocardiogram (ECG) signals are widely used in health care systems because they are non-invasive mechanisms to establish medical diagnosis of heart diseases. The current ECG systems suffer from important limitations: limited patient's mobility, limited energy, limited on wireless applications. The main drawback of current ECG systems is the location-specific nature of the .systems due to the use of fixed/wired applications. That is why; there is a critical need to improve the current ECG systems to cover security handling and to achieve extended patient's mobility. With this in mind, Compressed Sensing (CS) procedure and the collaboration of Block Sparse Bayesian Learning (BSBL) framework is used to provide a robust low sampling-rate approach for normal and abnormal ECG signals. Advanced WBANs based on our approach will be able to deliver healthcare not only to patients in hospital and medical centres; but also in their homes and workplaces thus offering cost saving, and improving the quality of life. Our simulation results based on two proposed algorithms illustrate 15% incensement of Signal to Noise Ratio (SNR) and a good level of quality for the degree of incoherence between the random measurement and sparsity matrices.