Machine Learning and Artificial Neural Network for Predicting Heart Failure Risk

Heart failure is now widely spread throughout the world.Heart disease affects approximately 48%of the population.It is too expensive and also difficult to cure the disease.This research paper represents machine learning models to predict heart failure.The fundamental concept is to compare the correctness of various Machine Learning(ML)algorithms and boost algorithms to improve models’accuracy for prediction.Some supervised algorithms like K-Nearest Neighbor(KNN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF),Logistic Regression(LR)are considered to achieve the best results.Some boosting algorithms like Extreme Gradient Boosting(XGBoost)and Cat-Boost are also used to improve the prediction using Artificial Neural Networks(ANN).This research also focuses on data visualization to identify patterns,trends,and outliers in a massive data set.Python and Scikit-learns are used for ML.Tensor Flow and Keras,along with Python,are used for ANN model train-ing.The DT and RF algorithms achieved the highest accuracy of 95%among the classifiers.Meanwhile,KNN obtained a second height accuracy of 93.33%.XGBoost had a gratified accuracy of 91.67%,SVM,CATBoost,and ANN had an accuracy of 90%,and LR had 88.33%accuracy.

Preparation of gradient biomaterial used for artificial mechanical heart valve

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An Artificial Heart System for Testing and Evaluation of Cardiac Pacemakers

The usability assessment of a pacemaker is a complex task where the dedicated programmer for testing programmed algorithms is necessary.This paper provides the outcomes of development and complex testing of the artificial cardiac system to evaluate the pacemaker’s functionality.In this work,we used the modular laboratory platform ELVIS II and created graphical user interface in LabVIEW programming environment.The electrical model of the heart allows signals generation(right atrium,right ventricle)and the monitoring of the stimulation pulses.The LabVIEW user interface allows to set the parameters of the generated signals and the simulation of the cardiac rhythm disorders as well as the monitoring and visualization of the pacemaker behavior in real-time.The results demonstrate the capability of proposed system to evaluate the paced and sensed pulses.The proposed solution allows the scientists to test the behavior of any cardiac pacemaker for its pre-programmed settings and pacing mode.In addition,the proposed system can simulate various disorders and test cardiac pacemakers in different working modes.

A NEWLY DUPLICATOR FOR PULSATILE AND STEADY FLOW TESTING OF ARTIFICIAL HEART VALVE

In order to go further into a question of the artificial heart valve, a pulsativeand steady flow test duplicator for the artificial heart valve (P. S. duplicator) has been developed by us. This duplicator can be used to measure the hydrodynamic parameters andcharacteristics of artiricial valves in vitro. So these measured valves can be assessed correctlyand precisely. The designing of the P. S. duplicator is raesonable and practical. Thus it cansatisfy the needs for the studying of various artificial valves.

Bleeding with the artificial heart: Gastrointestinal hemorrhage in CF-LVAD patients

Continuous-flow left ventricular assist devices(CF-LVADs)have significantly improved outcomes for patients with end-stage heart failure when used as a bridge to cardiac transplantation or,more recently,as destination therapy.However,its implantations carries a risk of complications including infection,device malfunction,arrhythmias,right ventricular failure,thromboembolic disease,postoperative and nonsurgical bleeding.A significant number of left ventricular assist devices(LVAD)recipients may experience recurrent gastrointestinal hemorrhage,mainly due to combination of antiplatelet and vitamin K antagonist therapy,activation of fibrinolytic pathway,acquired von Willebrand factor deficiency,and tendency to develop small intestinal angiodysplasias due to increased rotary speed of the pump.Gastrointestinal bleeding in LVAD patients remains a source of increased morbidity including the need for blood transfusions,extended hospital stays,multiple readmissions,and overall mortality.Management of gastrointestinal bleeding in LVAD patients involves multidisciplinary approach in stabilizing the patients,addressing risk factors and performing structured endoluminal evaluation with focus on upper gastrointestinal tract including jejunum to find and eradicate culprit lesion.Medical and procedural intervention is largely successful and universal bleeding cessation occurs in transplanted patients.

Artificial Intelligence in Medicine:Real Time Electronic Stethoscope for Heart Diseases Detection

Diseases of the cardiovascular system are one of the major causes of death worldwide.These diseases could be quickly detected by changes in the sound created by the action of the heart.This dynamic auscultations need extensive professional knowledge and emphasis on listening skills.There is also an unmet requirement for a compact cardiac condition early warning device.In this paper,we propose a prototype of a digital stethoscopic system for the diagnosis of cardiac abnormalities in real time using machine learning methods.This system consists of three subsystems that interact with each other(1)a portable digital subsystem of an electronic stethoscope,(2)a decision-making subsystem,and(3)a subsystemfor displaying and visualizing the results in an understandable form.The electronic stethoscope captures the patient’s phonocardiographic sounds,filters and digitizes them,and then sends the resulting phonocardiographic sounds to the decision-making system.The decision-making systemclassifies sounds into normal and abnormal using machine learning techniques,and as a result identifies abnormal heart sounds.The display and visualization subsystem demonstrates the results obtained in an understandable way not only for medical staff,but also for patients and recommends further actions to patients.As a result of the study,we obtained an electronic stethoscope that can diagnose cardiac abnormalities with an accuracy of more than 90%.More accurately,the proposed stethoscope can identify normal heart sounds with 93.5%accuracy,abnormal heart sounds with 93.25%accuracy.Moreover,speed is the key benefit of the proposed stethoscope as 15 s is adequate for examination.

Innovative Artificial Neural Networks-Based Decision Support System for Heart Diseases Diagnosis

Heart diagnosis is not always possible at every medical center, especially in the rural areas where less support and care, due to lack of advanced heart diagnosis equipment. Also, physician intuition and experience are not always sufficient to achieve high quality medical procedures results. Therefore, medical errors and undesirable results are reasons for a need for unconventional computer-based diagnosis systems, which in turns reduce medical fatal errors, increasing the patient safety and save lives. The proposed solution, which is based on an Artificial Neural Networks (ANNs), provides a decision support system to identify three main heart diseases: mitral stenosis, aortic stenosis and ventricular septal defect. Furthermore, the system deals with an encouraging opportunity to develop an operational screening and testing device for heart disease diagnosis and can deliver great assistance for clinicians to make advanced heart diagnosis. Using real medical data, series of experiments have been conducted to examine the performance and accuracy of the proposed solution. Compared results revealed that the system performance and accuracy are acceptable, with a heart diseases classification accuracy of 92%.

Automatic Heart Disease Diagnosis System Based on Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) Approaches

This paper aims to design and implement an automatic heart disease diagnosis system using?MATLAB. The Cleveland data set for heart diseases was used as the main database for training and testing the developed system. In order to train and test the Cleveland data set, two systems were developed. The first system is based on the Multilayer Perceptron (MLP) structure on the Artificial Neural Network (ANN), whereas the second system is based on the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) approach. Each system has two main modules, namely, training and testing,?where 80% and 20% of the Cleveland data set were randomly selected for training and testing?purposes respectively. Each system also has an additional module known as case-based module,?where the user has to input values for 13 required attributes as specified by the Cleveland data set,?in order to test the status of the patient whether heart disease is present or absent from that particular patient. In addition, the effects of different values for important parameters were investigated in the ANN-based and Neuro-Fuzzy-based systems in order to select the best parameters that obtain the highest performance. Based on the experimental work, it is clear that the Neuro-Fuzzy system outperforms the ANN system using the training data set, where the accuracy for each system was 100% and 90.74%, respectively. However, using the testing data set, it is clear that the ANN system outperforms the Neuro-Fuzzy system, where the best accuracy for each system was 87.04% and 75.93%, respectively.

Machine Learning-Based Intelligent Auscultation Techniques in Congenital Heart Disease:Application and Development

Congenital heart disease(CHD),the most prevalent congenital ailment,has seen advancements in the“dual indi-cator”screening program.This facilitates the early-stage diagnosis and treatment of children with CHD,subse-quently enhancing their survival rates.While cardiac auscultation offers an objective reflection of cardiac abnormalities and function,its evaluation is significantly influenced by personal experience and external factors,rendering it susceptible to misdiagnosis and omission.In recent years,continuous progress in artificial intelli-gence(AI)has enabled the digital acquisition,storage,and analysis of heart sound signals,paving the way for intelligent CHD auscultation-assisted diagnostic technology.Although there has been a surge in studies based on machine learning(ML)within CHD auscultation and diagnostic technology,most remain in the algorithmic research phase,relying on the implementation of specific datasets that still await verification in the clinical envir-onment.This paper provides an overview of the current stage of AI-assisted cardiac sounds(CS)auscultation technology,outlining the applications and limitations of AI auscultation technology in the CHD domain.The aim is to foster further development and refinement of AI auscultation technology for enhanced applications in CHD.

Mediating function of heart failure in the causal relationship between diastolic blood pressure and hypertensive renal disease with renal failure:a mediated Mendelian randomization study

Objective:To study the causality relationship between diastolic blood pressure(DBP)and hypertensive renal disease with renal failure(HRDRF)and the mediating role of hear t failure(HF)in the causality relationship by network Mendelian randomization(MR).Methods:Genome-wide analysis of DBP,HRDRF,and HF was downloaded from the public database(Genome-Wide Analysis Study[GWAS])and was used to analyze the results and to conduct mediated MR analysis.Results:Analysis showed that DBP was positively correlated with HRDRF(OR=1.0002,95%CI:1.0001–1.0003,P=1.8076e-05)and DBP was positively correlated with HF(OR=1.0295,95%CI:1.0221–1.0370,P=2.5292e-15).HF and HRDRF had a positive causal effect(OR=1.0001,95%CI:1.0000–1.0001,P=0.0152).Mediation analysis showed that the contribution ratio of HF to the combined effect of DBP and HRDRF was 24.69%.Conclusions:DBP can increase the risk of renal disease with renal failure,and HF may play an impor tant role in mediating this causal relationship.

Artificial intelligence-assisted repair of peripheral nerve injury: a new research hotspot and associated challenges

Artificial intelligence can be indirectly applied to the repair of peripheral nerve injury.Specifically,it can be used to analyze and process data regarding peripheral nerve injury and repair,while study findings on peripheral nerve injury and repair can provide valuable data to enrich artificial intelligence algorithms.To investigate advances in the use of artificial intelligence in the diagnosis,rehabilitation,and scientific examination of peripheral nerve injury,we used CiteSpace and VOSviewer software to analyze the relevant literature included in the Web of Science from 1994–2023.We identified the following research hotspots in peripheral nerve injury and repair:(1)diagnosis,classification,and prognostic assessment of peripheral nerve injury using neuroimaging and artificial intelligence techniques,such as corneal confocal microscopy and coherent anti-Stokes Raman spectroscopy;(2)motion control and rehabilitation following peripheral nerve injury using artificial neural networks and machine learning algorithms,such as wearable devices and assisted wheelchair systems;(3)improving the accuracy and effectiveness of peripheral nerve electrical stimulation therapy using artificial intelligence techniques combined with deep learning,such as implantable peripheral nerve interfaces;(4)the application of artificial intelligence technology to brain-machine interfaces for disabled patients and those with reduced mobility,enabling them to control devices such as networked hand prostheses;(5)artificial intelligence robots that can replace doctors in certain procedures during surgery or rehabilitation,thereby reducing surgical risk and complications,and facilitating postoperative recovery.Although artificial intelligence has shown many benefits and potential applications in peripheral nerve injury and repair,there are some limitations to this technology,such as the consequences of missing or imbalanced data,low data accuracy and reproducibility,and ethical issues(e.g.,privacy,data security,research transparency).Future research should address the issue of data collection,as large-scale,high-quality clinical datasets are required to establish effective artificial intelligence models.Multimodal data processing is also necessary,along with interdisciplinary collaboration,medical-industrial integration,and multicenter,large-sample clinical studies.

Serum cystatin C,monocyte/high-density lipoprotein-C ratio,and uric acid for the diagnosis of coronary heart disease and heart failure

BACKGROUND Coronary heart disease(CHD)and heart failure(HF)are the major causes of morbidity and mortality worldwide.Early and accurate diagnoses of CHD and HF are essential for optimal management and prognosis.However,conventional diagnostic methods such as electrocardiography,echocardiography,and cardiac biomarkers have certain limitations,such as low sensitivity,specificity,availability,and cost-effectiveness.Therefore,there is a need for simple,noninvasive,and reliable biomarkers to diagnose CHD and HF.AIM To investigate serum cystatin C(Cys-C),monocyte/high-density lipoprotein cholesterol ratio(MHR),and uric acid(UA)diagnostic values for CHD and HF.METHODS We enrolled 80 patients with suspected CHD or HF who were admitted to our hospital between July 2022 and July 2023.The patients were divided into CHD(n=20),HF(n=20),CHD+HF(n=20),and control groups(n=20).The serum levels of Cys-C,MHR,and UA were measured using immunonephelometry and an enzymatic method,respectively,and the diagnostic values for CHD and HF were evaluated using receiver operating characteristic(ROC)curve analysis.RESULTS Serum levels of Cys-C,MHR,and UA were significantly higher in the CHD,HF,and CHD+HF groups than those in the control group.The serum levels of Cys-C,MHR,and UA were significantly higher in the CHD+HF group than those in the CHD or HF group.The ROC curve analysis showed that serum Cys-C,MHR,and UA had good diagnostic performance for CHD and HF,with areas under the curve ranging from 0.78 to 0.93.The optimal cutoff values of serum Cys-C,MHR,and UA for diagnosing CHD,HF,and CHD+HF were 1.2 mg/L,0.9×10^(9),and 389μmol/L;1.4 mg/L,1.0×10^(9),and 449μmol/L;and 1.6 mg/L,1.1×10^(9),and 508μmol/L,respectively.CONCLUSION Serum Cys-C,MHR,and UA are useful biomarkers for diagnosing CHD and HF,and CHD+HF.These can provide information for decision-making and risk stratification in patients with CHD and HF.

The Problem of Rehospitalisation for Heart Failure at the Cardiology Department of the Hôpital National Ignace Deen

Introduction: Despite current therapeutic advances, heart failure in sub-Saharan Africa remains a common, serious and costly disease, particularly due to rehospitalizations. The objective of this work was to determine the proportion of rehospitalizations for heart failure and to identify etiological factors. Methodology: This was a retrospective descriptive study with a duration of 8 months from April 1 to November 30, 2021. This study included all patients rehospitalized in the department for Heart Failure and who agreed to participate in our study. Results: During the period of our study, 437 patients were hospitalized in the HF department, among which we collected 126 cases of rehospitalization for HF with a frequency of 28.83%. The mean age of our patients was 46.32 ± 18.98 years with the extremes of 15 to 84 years. The most affected age group was between 35 and 44 years old in 24 cases, i.e. a frequency of 19%. We observed a female predominance of 64 cases, i.e. a frequency of 50.8% compared to 62 cases, i.e. a frequency of 49.2% with a sex ratio (M/F) equal to 0.96. 98 cases of our patients, i.e. a frequency of 77.8%, were mutual insurance companies who felt they had the necessary support from those around them. In our sample, the underlying heart disease was mainly represented by valvular heart disease in 59 cases, followed by hypertensive heart disease in 42 cases with the respective frequencies of 46.82% and 33.33%. The majority of our patients were rehospitalized (1 - 3) times after a first episode of HF flare-up in 117 cases or 92.9%. Irregularity at control and therapeutic break were the most common decompensation factors with frequencies of 75.8% and 74.2% respectively. The majority of our patients were rehospitalized (1 - 3) times after a first episode of HF flare-up in 117 cases or 92.9%. Irregularity at control and therapeutic break were the most common decompensation factors with frequencies of 75.8% and 74.2% respectively. Conclusion: It appears in this study that rehospitalizations for heart failure are frequent, linked to irregularity in control and the lack of therapeutic education.

Explainable Artificial Intelligence(XAI)Model for Cancer Image Classification

The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare environments.It is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these algorithms.These models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial intelligence.Nevertheless,the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer images.This research presents an advanced investigation of XAI models to classify cancer images.It describes the different levels of explainability and interpretability associated with XAI models and the challenges faced in deploying them in healthcare applications.In addition,this study proposes a novel framework for cancer image classification that incorporates XAI models with deep learning and advanced medical imaging techniques.The proposed model integrates several techniques,including end-to-end explainable evaluation,rule-based explanation,and useradaptive explanation.The proposed XAI reaches 97.72%accuracy,90.72%precision,93.72%recall,96.72%F1-score,9.55%FDR,9.66%FOR,and 91.18%DOR.It will discuss the potential applications of the proposed XAI models in the smart healthcare environment.It will help ensure trust and accountability in AI-based decisions,which is essential for achieving a safe and reliable smart healthcare environment.

Artificial Intelligence Prediction of One-Part Geopolymer Compressive Strength for Sustainable Concrete

Alkali-activated materials/geopolymer(AAMs),due to their low carbon emission content,have been the focus of recent studies on ecological concrete.In terms of performance,fly ash and slag are preferredmaterials for precursors for developing a one-part geopolymer.However,determining the optimum content of the input parameters to obtain adequate performance is quite challenging and scarcely reported.Therefore,in this study,machine learning methods such as artificial neural networks(ANN)and gene expression programming(GEP)models were developed usingMATLAB and GeneXprotools,respectively,for the prediction of compressive strength under variable input materials and content for fly ash and slag-based one-part geopolymer.The database for this study contains 171 points extracted from literature with input parameters:fly ash concentration,slag content,calcium hydroxide content,sodium oxide dose,water binder ratio,and curing temperature.The performance of the two models was evaluated under various statistical indices,namely correlation coefficient(R),mean absolute error(MAE),and rootmean square error(RMSE).In terms of the strength prediction efficacy of a one-part geopolymer,ANN outperformed GEP.Sensitivity and parametric analysis were also performed to identify the significant contributor to strength.According to a sensitivity analysis,the activator and slag contents had the most effects on the compressive strength at 28 days.The water binder ratio was shown to be directly connected to activator percentage,slag percentage,and calcium hydroxide percentage and inversely related to compressive strength at 28 days and curing temperature.

Advancements in Barrett's esophagus detection:The role of artificial intelligence and its implications

Artificial intelligence(AI)is making significant strides in revolutionizing the detection of Barrett's esophagus(BE),a precursor to esophageal adenocarcinoma.In the research article by Tsai et al,researchers utilized endoscopic images to train an AI model,challenging the traditional distinction between endoscopic and histological BE.This approach yielded remarkable results,with the AI system achieving an accuracy of 94.37%,sensitivity of 94.29%,and specificity of 94.44%.The study's extensive dataset enhances the AI model's practicality,offering valuable support to endoscopists by minimizing unnecessary biopsies.However,questions about the applicability to different endoscopic systems remain.The study underscores the potential of AI in BE detection while highlighting the need for further research to assess its adaptability to diverse clinical settings.

Artificial intelligence in individualized retinal disease management

Owing to the rapid development of modern computer technologies,artificial intelligence(AI)has emerged as an essential instrument for intelligent analysis across a range of fields.AI has been proven to be highly effective in ophthalmology,where it is frequently used for identifying,diagnosing,and typing retinal diseases.An increasing number of researchers have begun to comprehensively map patients’retinal diseases using AI,which has made individualized clinical prediction and treatment possible.These include prognostic improvement,risk prediction,progression assessment,and interventional therapies for retinal diseases.Researchers have used a range of input data methods to increase the accuracy and dependability of the results,including the use of tabular,textual,or image-based input data.They also combined the analyses of multiple types of input data.To give ophthalmologists access to precise,individualized,and high-quality treatment strategies that will further optimize treatment outcomes,this review summarizes the latest findings in AI research related to the prediction and guidance of clinical diagnosis and treatment of retinal diseases.

Health Systems Strengthening to Tackle the Global Burden of Pediatric and Congenital Heart Disease: A Diagonal Approach

1 Background Congenital heart disease(CHD)is the most common major congenital anomaly,affecting approximately one in every 100 live births[1].Among congenital anomalies,66%of preventable deaths are due to CHD,and 58%of the avertable morbidity and mortality due to congenital anomalies would result from scaling congenital heart surgery services[2].Every year,nearly 300,000 children and adults die from CHD,the majority of whom live in low-and middle-income countries(LMICs)[3].Approximately 49%of all individuals with CHD will require surgical or interventional care at some point in their lifetime[4];as a result of advances in access to and the delivery of such services,over 95%of children born with CHD in high-income countries now live into adulthood[3].Here,adults have surpassed children in the number of CHD cases at a ratio of 2:1[5].

Congenital heart“Challenges”in Down syndrome

In this editorial,we comment on the article by Kong et al published in the recent issue of the World Journal of Cardiology.In this interesting case,the authors present the challenges faced in managing a 13-year-old patient with Down syndrome(DS)and congenital heart disease(CHD)associated with pulmonary arterial hypertension.In this distinct population,the Authors underscore the need for early diagnosis and management as well as the need of a multidisciplinary approach for decision making.It seems that the occurrence of CHD in patients with DS adds layers of complexity to their clinical management.This editorial aims to provide a comprehensive overview of the intricate interplay between DS and congenital heart disorders,offering insights into the nuanced diagnostic and therapeutic considerations for physicians.

Electroacupuncture improves myocardial fibrosis in heart failure rats by attenuating ECM collagen deposition through modulation of TGF-β1/Smads signaling pathway

Background: To explore the effects of electroacupuncture on cardiac function and myocardial fibrosis in rat models of heart failure, and to elucidate the underlying mechanism of electroacupuncture in heart failure treatment. Methods: Healthy male Sprague-Dawley rats were allocated into three groups: Sham group, Model group, and electroacupuncture (Model + EA) group, with each group comprising 8 rats. The model underwent a procedure involving the ligation of the left anterior descending coronary artery to induce a model of heart failure. The Model + EA group was used for 7 consecutive days for electroacupuncture of bilateral Shenmen (HT7) and Tongli (HT5), once a day for 30 min each time. Left ventricular parameters in rats were assessed using a small-animal ultrasound machine to analyze changes in left ventricular end-diastolic volume, left ventricular end-systolic volume, left ventricular ejection fraction, and left ventricular fractional shortening. Serum interleukin-1β (IL-1β), cardiac troponin (cTn), and N-terminal brain natriuretic peptide precursor levels were measured using ELISA. Histopathological changes in rat myocardium were observed through HE staining, while collagen deposition in rat myocardial tissue was assessed using the Masson staining method. Picro sirius red staining, immunohistochemical staining, and RT-qPCR were utilized to distinguish between the various types of collagen deposition. The expression level of TGF-β1 and SMAD2/3/4/7 mRNA in rat myocardial tissues was determined using RT-qPCR. Additionally, western blot analysis was conducted to assess the protein expression levels of TGF-β1, SMAD3/7, and p-SMAD3 in rat myocardial tissues. Results: Compared with the Sham group, the left ventricular ejection fraction and left ventricular fractional shortening values of the Model group were significantly decreased (P < 0.01);the left ventricular end-diastolic volume and left ventricular end-systolic volume values were remarkably increased (P < 0.01);serum N-terminal brain natriuretic peptide precursor content was increased (P < 0.01);serum IL-1β and cTn levels were increased (P < 0.01);myocardial collagen volume fraction were increased (P < 0.01);and those of the expression of TGF-β1 and SMAD2/3/4 mRNA was increased (P < 0.01);the expression of SMAD7 mRNA was decreased (P < 0.01);the protein expression levels of TGF-β1, SMAD3, and p-Smad3 were increased (P < 0.01);the protein expression level of SMAD7 was decreased (P < 0.01) in the Model group. Compared to the Model group, the expression levels of the proteins TGF-β1, SMAD3, and p-Smad3 in myocardial tissue were found to be decreased (P < 0.01), and the expression level of the protein SMAD7 was found to be increased (P < 0.01) in the Model + EA group;the collagen volume fraction and deposition of type Ⅰ /Ⅲ collagen were decreased (P < 0.01) in the Model + EA group. Conclusion: Electroacupuncture alleviates myocardial fibrosis in rats with heart failure, and this effect is likely due to attributed to the modulation of the TGF-β1/Smads signaling pathway, which helps reduce collagen deposition in the extracellular matrix.