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با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.

نتیجه جستجو - Heart disease

تعداد مقالات یافته شده: 16
ردیف عنوان نوع
1 Can the development of a patient’s condition be predicted through intelligent inquiry under the e-health business mode? Sequential feature map-based disease risk prediction upon features selected from cognitive diagnosis big dat
آیا می توان از طریق استعلام هوشمند تحت شرایط تجارت الکترونیکی ، وضعیت یک بیمار را پیش بینی کرد؟ پیش بینی خطر ابتلا به بیماری مبتنی بر ویژگی های توالی بر ویژگی های انتخاب شده از تشخیص شناختی داده های بزرگ-2020
The data-driven mode has promoted the researches of preventive medicine. In prediction of disease risks, physicians’ clinical cognitive diagnosis data can be used for early prevention of diseases and, therefore, to reduce medical cost, to improve accessibility of medical services and to lower medical risk. However, researches involved no physicians’ cognition of patients’ conditions in intelligent inquiry under e-health business mode, offered no diagnosis big data, neglected the values of the fused text information generated by joint activities of online and offline medical data, and failed to thoroughly analyze the phenomenon of redundancy-complementarity dispersion caused by high-order information shortage from the online inquiry data-driven perspective. Besides, the risk prediction simply based on offline clinical cognitive diagnosis data undoubtedly reduces prediction precision. Importantly, relevant researches rarely considered temporal relationships of different medical events, did not conduct detailed analysis on practical problems of pattern explosion, did not offer a thought of intelligent portrayal map, and did not conduct relevant risk prediction based on the sub-maps obtained from the map. In consequence, the paper presents a disease risk prediction method with the model for redundancy-complementarity dispersion-based feature selection from physicians’ online cognitive diagnosis big data to realize features selection from the cognitive diagnosis big data of online intelligent inquiry; the obtained features were ranked intelligently for subsequent high-dimensional information shortage compensation; the compensated key feature information of the cognitive diagnosis big data was fused with offline electronic medical record (EMR) to form the virtual electronic medical record (VEMR). The formed VEMR was combined with the method of the sequential feature map for modelling, and a sequential feature map-based model for disease risk prediction was presented to obtain online users’ medical conditions. A neighborhood-based collaborative prediction model was presented for prediction of an online intelligent medical inquiry user’s possible diseases in the future and to intelligently rank the risk probabilities of the diseases. In the experiments, the online intelligent medical inquiry users’ VEMRs were used as the foundation of the simulation experiments to predict disease risks in chronic obstructive pulmonary disease (OCPD) population and rheumatic heart disease (RHD) population. The experiments demonstrated that the presented method showed relatively good metric performances in the VEMR and improved disease risk prediction.
Keywords: Cognitive diagnosis big data | Online intelligent inquiry | Sequential feature map | Disease risk prediction | Redundancy and complementarity dispersion
مقاله انگلیسی
2 HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments
HealthFog: یک سیستم هوشمند درمانی هوشمند مبتنی بر یادگیری عمیق برای تشخیص خودکار بیماری های قلبی در محیط های IoT و محاسبات مه-2020
Cloud computing provides resources over the Internet and allows a plethora of applications to be deployed to provide services for different industries. The major bottleneck being faced currently in these cloud frameworks is their limited scalability and hence inability to cater to the requirements of centralized Internet of Things (IoT) based compute environments. The main reason for this is that latency-sensitive applications like health monitoring and surveillance systems now require computation over large amounts of data (Big Data) transferred to centralized database and from database to cloud data centers which leads to drop in performance of such systems. The new paradigms of fog and edge computing provide innovative solutions by bringing resources closer to the user and provide low latency and energy efficient solutions for data processing compared to cloud domains. Still, the current fog models have many limitations and focus from a limited perspective on either accuracy of results or reduced response time but not both. We proposed a novel framework called HealthFog for integrating ensemble deep learning in Edge computing devices and deployed it for a real-life application of automatic Heart Disease analysis. HealthFog delivers healthcare as a fog service using IoT devices and efficiently manages the data of heart patients, which comes as user requests. Fog-enabled cloud framework, FogBus is used to deploy and test the performance of the proposed model in terms of power consumption, network bandwidth, latency, jitter, accuracy and execution time. HealthFog is configurable to various operation modes which provide the best Quality of Service or prediction accuracy, as required, in diverse fog computation scenarios and for different user requirements.
Keywords: Fog computing | Internet of things | Healthcare | Deep learning | Ensemble learning | Heart patient analysis
مقاله انگلیسی
3 Predictive model of cardiac arrest in smokers using machine learning technique based on Heart Rate Variability parameter
مدل پیش بینی ایست قلبی در افراد سیگاری با استفاده از روش یادگیری ماشین بر اساس پارامتر تنوع ضربان قلب-2019
Cardiac arrest is a severe heart anomaly that results in billions of annual casualties. Smoking is a specific hazard factor for cardiovascular pathology, including coronary heart disease, but data on smoking and heart death not earlier reviewed. The Heart Rate Variability (HRV) parameters used to predict cardiac arrest in smokers using machine learning technique in this paper. Machine learning is a method of computing experience based on automatic learning and enhances performances to increase prognosis. This study intends to compare the performance of logistical regression, decision tree, and random forest model to predict cardiac arrest in smokers. In this paper, a machine learning technique implemented on the dataset received from the data science research group MITU Skillogies Pune, India. To know the patient has a chance of cardiac arrest or not, developed three predictive models as 19 input feature of HRV indices and two output classes. These model evaluated based on their accuracy, precision, sensitivity, specificity, F1 score, and Area under the curve (AUC). The model of logistic regression has achieved an accuracy of 88.50%, precision of 83.11%, the sensitivity of 91.79%, the specificity of 86.03%, F1 score of 0.87, and AUC of 0.88. The decision tree model has arrived with an accuracy of 92.59%, precision of 97.29%, the sensitivity of 90.11%, the specificity of 97.38%, F1 score of 0.93, and AUC of 0.94. The model of the random forest has achieved an accuracy of 93.61%, precision of 94.59%, the sensitivity of 92.11%, the specificity of 95.03%, F1 score of 0.93 and AUC of 0.95. The random forest model achieved the best accuracy classification, followed by the decision tree, and logistic regression shows the lowest classification accuracy.
Keywords: Cardiac arrest | Heart Rate Variability | Machine learning | Accuracy | Precision | Area under the curve
مقاله انگلیسی
4 Identification of significant features and data mining techniques in predicting heart disease
شناسایی ویژگی های قابل توجه و تکنیک های داده کاوی در پیش بینی بیماری قلبی-2019
Cardiovascular disease is one of the biggest cause for morbidity and mortality among the population of the world. Prediction of cardiovascular disease is regarded as one of the most important subject in the section of clinical data analysis. The amount of data in the healthcare industry is huge. Data mining turns the large collection of raw healthcare data into information that can help to make informed decision and prediction. There are some existing studies that applied data mining techniques in heart disease prediction. Nonetheless, studies that have given attention towards the significant features that play a vital role in predicting cardiovascular disease are limited. It is crucial to select the correct combination of significant features that can improve the performance of the prediction models. This research aims to identify significant features and data mining techniques that can improve the accuracy of predicting cardiovascular disease. Prediction models were developed using different combination of features, and seven classification techniques: k-NN, Decision Tree, Naive Bayes, Logistic Regression (LR), Support Vector Machine (SVM), Neural Network and Vote (a hybrid technique with Naïve Bayes and Logistic Regression). Experiment results show that the heart disease prediction model developed using the identified significant features and the best-performing data mining technique (i.e. Vote) achieves an accuracy of 87.4% in heart disease prediction.
Keywords: Data mining | Prediction model | Classification algorithms | Feature selection | Heart disease prediction
مقاله انگلیسی
5 A machine learning-based approach for predicting the outbreak of cardiovascular diseases in patients on dialysis
روش مبتنی بر یادگیری ماشینی برای پیش بینی شیوع بیماریهای قلبی عروقی در بیماران مبتلا به دیالیز-2019
Background and Objective: Patients with End- Stage Kidney Disease (ESKD) have a unique cardiovascular risk. This study aims at predicting, with a certain precision, death and cardiovascular diseases in dialysis patients. Methods: To achieve our aim, machine learning techniques have been used. Two datasets have been taken into consideration: the first is an Italian dataset obtained from the Istituto di Fisiologia Clinica of Consiglio Nazionale delle Ricerche of Reggio Calabria; the second is an American dataset provided by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) repository. From each one we obtained 5 datasets, according to the outcome of interest. We tested different types of algorithm (both linear and non-linear), but the final choice was to use Support Vector Machine. In particular, we obtained the best performances using the non-linear SVC with RBF kernel algorithm, optimizing it with GridSearch. The last is an algorithm useful to search the best combination of hyper-parameters (in our case, to find the best couple (C, γ)), in order to improve the accuracy of the algorithm. Results: The use of non-linear SVC with RBF kernel algorithm, optimized with GridSearch, allowed to obtain an accuracy of 95.25% in the Italian dataset and of 92.15% in the American dataset, in a timeframe of 2.5 years,in the prediction of Ischaemic Heart Disease. A worse performance was obtained for the other outcomes. Conclusions: The machine learning-based approach applied in our study is able to predict, with a high accuracy, the outbreak of cardiovascular diseases in patients on dialysis.
Keywords: Machine learning | Cardiovascular outcomes | ESRD | Prognosis
مقاله انگلیسی
6 استفاده از شبکه های عصبی موجی فازی تابعی ترکیبی با یک الگوریتم بهینه سازی مبتنی بر تدریس – یادگیری برای تشخیص بیماری پزشکی
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 15 - تعداد صفحات فایل doc فارسی: 58
تشخیص صحیح بیماری پزشکی، یک مسئله مهم در دسته بندی تلقی می شود. هدف اصلی فرآیند دسته بندی، تعیین دسته ای است که یک الگوی خاص به آن تعلق دارد. در این مقاله یک روش دسته بندی جدید برمبنای ترکیبی از الگوریتم بهینه سازی مبتنی بر تدریس – یادگیری (TLBO) و شبکه عصبی موجی فازی (FWNN) با شبکه عصبی ارتباطی تابعی (FLNN)، پیشنهاد می شود. به علاوه، از الگوریتم TLBO برای راه اندازی شبکه عصبی موجی فازی تابعی ترکیبی جدید (FFWNN) و بهینه سازی پارامترهای یادگیری که عبارتند از وزن، اتساع و ترجمه، استفاده می شود. برای ارزیابی عملکرد روش پیشنهادی، از 5 سری داده پزشکی استاندارد استفاده شد: سرطان سینه، بیماری قلبی، هپاتیت، دیابت پیمای هندی و آپاندیس. کارآیی روش پیشنهادی با استفاده از اعتبارسنجی تقاطعی 5 باره و اعتبارسنجی تقاطعی 10 باره ازنظر مربع خطای میانگین، دقت دسته بندی، زمان اجرا، حساسیت، اختصاصی بودن و کاپا بررسی می شود. نتایج آزمایش نشان می دهند که کارآیی روش پیشنهادی برای مسئله های دسته بندی پزشکی برای سری های داده ای سرطان سینه، بیماری قلبی، هپاتیت، بیماری های پیمای هندی و آپاندیس ازنظر دقت پس از 30 اجرا برای هر سری داده ای با پیچیدگی محاسباتی پایین، به ترتیب برابر با 309/98، 1/91، 39/91، 67/88 و 51/93 درصد می باشد. به علاوه، مشاهده شده است که روش پیشنهادی درمقایسه با عملکرد سایر روشهای یافت شده در مطالعات قبلی مرتبط، عملکرد کارآمدی دارد.
کلیدواژه ها: شبکه عصبی موجی فازی (FWNN) | شبکه عصبی ارتباطی تابعی (FLNN) | الگوریتم بهینه سازی مبتنی بر تدریس- یادگیری (TLBO) | شبکه عصبی موجی فازی تابعی (FFWNN)
مقاله ترجمه شده
7 Probabilistic grammar-based neuroevolution for physiological signal classification of ventricular tachycardia
Probabilistic grammar-based neuroevolution for physiological signal classification of ventricular tachycardia-2019
Ventricular tachycardia is a rapid heart rhythm that begins in the lower chambers of the heart. When it happens continuously, this may result in life-threatening cardiac arrest. In this paper, we apply deep learning techniques to tackle the problem of the physiological signal classification of ventricular tachy- cardia, since deep learning techniques can attain outstanding performance in many medical applications. Nevertheless, human engineers are required to manually design deep neural networks to handle differ- ent tasks. This can be challenging because of many possible deep neural network structures. Therefore, a method, called ADAG-DNE, is presented to automatically design deep neural network structures using deep neuroevolution. Our approach defines a set of structures using probabilistic grammar and searches for best network structures using Probabilistic Model Building Genetic Programming. ADAG-DNE takes advantages of the probabilistic dependencies found among the structures of networks. When applying ADAG-DNE to the classification problem, our discovered model achieves better accuracy than AlexNet, ResNet, and seven non-neural network classifiers. It also uses about 2% of parameters of AlexNet, which means the inference can be made quickly. To summarize, our method evolves a deep neural network, which can be implemented in expert systems. The deep neural network achieves high accuracy. Moreover, it is simpler than existing deep neural networks. Thus, computational efficiency and diagnosis accuracy of the expert system can be improved.
Keywords: Physiological signal classification | Heart disease | Neuroevolution | Probabilistic grammar | Genetic programming | Deep neural network
مقاله انگلیسی
8 A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system
معماری جدید اینترنت اشیاء و اکوسیستم داده های بزرگ برای نظارت بر سیستم مراقبت سلامت هوشمند و سیستم هشدار دهنده امن-2018
Wearable medical devices with sensor continuously generate enormous data which is often called as big data mixed with structured and unstructured data. Due to the complexity of the data, it is difficult to process and analyze the big data for finding valuable information that can be useful in decision making. On the other hand, data security is a key requirement in healthcare big data system. In order to overcome this issue, this paper proposes a new architecture for the implementation of IoT to store and process scalable sensor data (big data) for health care applications. The Proposed architecture consists of two main sub architectures, namely, Meta Fog-Redirection (MF-R) and Grouping and Choosing (GC) architecture. MF-R architecture uses big data technologies such as Apache Pig and Apache HBase for collection and storage of the sensor data (big data) generated from different sensor devices. The proposed GC architecture is used for securing integration of fog computing with cloud computing. This architecture also uses key management service and data categorization function (Sensitive, Critical and Normal) for providing security services. The framework also uses MapReduce based prediction model to predict the heart diseases. Performance evaluation parameters such as throughput, sensitivity, accuracy, and f-measure are calculated to prove the efficiency of the proposed architecture as well as the prediction model.
Keywords: Wireless sensor networks ، Internet of Things ، Big data analytics ، Cloud computing and health car
مقاله انگلیسی
9 ارزیابی کارایی تکنیک های طبقه بندی داده کاوی برای پیش بینی بیماری قلبی
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 12
بیماری قلبی ممکن است یکی از دلایل اصلی مرگ باشد. به علت فقدان دانش و تجربیات متخصصان درمورد علائم نارسایی قلب برای پیش بینی اولیه این بیماری، کار آسان برای تشخیص بیماری نیست. در نتیجه، پیش بینی مبتنی بر رایانه؛ مبتلایان به بیماری قلبی می تواند نقش مهمی را در تشخیص پیش از مرحله برای انجام اقدامات مناسب با توجه به بهبودی بیماران بازی کند. با این حال، انتخاب روش طبقه بندی مناسب داده کاوی می تواند به طور موثر پیش بینی مرحله اولیه بیماری را برای بازگشت از آن به همراه داشته باشد. در این مقاله، سه تکنیک طبقه بندی استفاده شده غالب از قبیل ماشین بردار پشتیبانی (SVM)، نزدیکترین همسایۀ k (KNN) و شبکه عصبی مصنوعی (ANN) را مورد بررسی قرار می دهیم، با توجه به ارزیابی آنها برای پیش بینی بیماری های قلبی با استفاده از مجموعه داده های بیماری کلیوی استاندارد مورد مطالعه قرار گرفته است.. نتایج تجربی نشان می دهد که دقت طبقه بندی با استفاده از SVM (85.1852٪) بهتر از استفاده از KNN (82663٪) و ANN (73.3333٪) است.
لغات کلیدی: داده کاوی | ماشین بردار پشتیبانی | نزدیکترین همسایۀ k | شبکه عصبی مصنوعی | پیش بینی بیماری قلبی | تکنیک های طبقه بندی
مقاله ترجمه شده
10 Feature extraction through parallel Probabilistic Principal Component Analysis for heart disease diagnosis
استخراج ویژگی از طریق تجزیه و تحلیل مولفه های موازی احتمالی برای تشخیص بیماری های قلبی-2017
Automatic diagnosis of human diseases are mostly achieved through decision support systems. The performance of these systems is mainly dependent on the selection of the most relevant features. This becomes harder when the dataset contains missing values for the different features. Probabilistic Principal Component Analysis (PPCA) has reputation to deal with the problem of missing values of attributes. This research presents a methodology which uses the results of medical tests as input, extracts a reduced dimensional feature subset and provides diagnosis of heart disease. The proposed methodology extracts high impact features in new projection by using Probabilistic Principal Component Analysis (PPCA). PPCA extracts projection vectors which contribute in highest covariance and these projection vectors are used to reduce feature dimension. The selection of projection vectors is done through Parallel Analysis (PA). The feature subset with the reduced dimension is provided to radial basis function (RBF) kernel based Support Vector Machines (SVM). The RBF based SVM serves the purpose of classification into two categories i.e., Heart Patient (HP) and Normal Subject (NS). The proposed methodology is evaluated through accuracy, specificity and sensitivity over the three datasets of UCI i.e., Cleveland, Switzer land and Hungarian. The statistical results achieved through the proposed technique are presented in comparison to the existing research showing its impact. The proposed technique achieved an accuracy of 82.18%, 85.82% and 91.30% for Cleveland, Hungarian and Switzerland dataset respectively.
Keywords: Heart disease | Feature extraction | Parallel analysis | Probabilistic PCA | SVM
مقاله انگلیسی
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