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
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
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
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
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|
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
Management of obesity in adult Asian Indians
مدیریت چاقی در بزرگسالان هندی آسیایی-2017
Theprevalenceof obesity in Indiais increasing and ranges from8% to 38%in rural and 13% to 50%in urban areas. Obesity is a risk factor for development of type 2 diabetes mellitus (T2DM), hypertension, dyslipidemia, coronary heart disease and many cancers. In Asian Indians excess abdominal and hepatic fat is associated with increased risk for T2DM and cardiovasculardisease. There is higher risk fordevelopmentof obesity related non communicable diseases at lower body mass index levels, compared to white Caucasians. Despite being a commonly encountered medical problem, obesity poses challenges in treatment. Many Indian physicians find themselves to be lacking time and expertise to prepare an appropriate obesity management plan and patients experience continuous weight gain over time despite being under regular medical supervision. In this article, we outline approaches to obesity management in ‘real life mode’ and in context to Asian Indian patients.
Keywords: Obesity | Management | Asian Indians | Lifestyle intervention | Pharmacotherapy
Review of the Society of Thoracic Surgeons Congenital Heart Surgery Database: 2017 Update on Outcomes and Quality Implications for the Anesthesiologist
مرور پایگاه داده جراحی انجمن جراحان قلب مادرزادی : 2017 به روز رسانی نتایج و پیامدهای کیفیت برای متخصص بیهوشی-2017
The pathway to current state-of-the-art congenital cardiac perioperative care has been paved by pioneering heart surgery, the development of dedicated cardiac intensive care units, as well as congenital cardiology and anesthesiology care teams. We work in an era of large datasets where data collection and analysis influence medical decision making. After three decades of data collection, analysis and subsequent changes in clinical management, progress is being made in developing data-driven care protocols in patients with congenital heart disease.1 The term standardization in reference to medical care can be misinterpreted as a “one size fits all” mentality. Medical care should be individualized to a specific patient, whereas data analyses and review of patient outcomes potentially decrease unnecessary variation in care. Additionally, comparative data has helped influence important concepts such as center transparency and public reporting in congenital heart surgery. Centers can access data from the Society of Thoracic Surgeons website since January 2015.
Outcomes | quality | database | congenital heart disease | anesthesia | cardiac surgery
A Big Data Approach for Proactive Healthcare Monitoring of Chronic Patients
یک رویکرد بزرگ داده برای نظارت بر مراقبت های بهداشتی پیشگیرانه از بیماران مزمن-2016
With the advancement of human life, there is big increase in chronic diseases especially in heart diseases. This increase is due to environmental pollution, lack of exercise, eating habits and closed environments. We propose a proactive health monitoring system for cardiac patients. This system consists of electronic bands wear by the patients to collect the real-time health status and an e-health system to process the collected data. The e-health system is a complete big data framework that will be develop to solve major healthcare problems. This system will provide a proactive measure to all the patients suffering from heart diseases like Ischemic heart disease or Hypertensive heart disease. The system will monitor the patient health continuously from electronic band and generates alerts to patients and doctor accordingly. This system will also provide a guidance to patients in case of abnormal health readings. The framework of e-health will follow the best practices of software engineering, healthcare and big data. This system will help patients to take proactive measure against any abnormal behavior in their health and also help doctors to monitor the patient health continuously. This development will enhance the quality of life of patients as well as increase the performance of doctors and healthcare providers.
Keywords: Biomedical monitoring | Monitoring | Diseases | Big data | Temperature sensors | Heart
Contrasting cardiovascular mortality trends in Eastern Mediterranean populations: Contributions from risk factor changes and treatments
تقابل روند مرگ و میر قلبی عروقی در جمعیت مدیترانه شرقی: سهم تغییرات عوامل خطر و درمان-2016
BACKGROUND: Middle income countries are facing an epidemic of non-communicable diseases, especially coronary heart disease (CHD). We used a validated CHD mortality model (IMPACT) to explain recent trends in Tunisia, Syria, the occupied Palestinian territory (oPt) and Turkey.
METHODS: Data on populations, mortality, patient numbers, treatments and risk factor trends from national and local surveys in each country were collated over two time points (1995-97; 2006-09); integrated and analysed using the IMPACT model.
RESULTS: Risk factor trends: Smoking prevalence was high in men, persisting in Syria but decreasing in Tunisia, oPt and Turkey. BMI rose by 1-2kg/m(2) and diabetes prevalence increased by 40%-50%. Mean systolic blood pressure and cholesterol levels increased in Tunisia and Syria. Mortality trends: Age-standardised CHD mortality rates rose by 20% in Tunisia and 62% in Syria. Much of this increase (79% and 72% respectively) was attributed to adverse trends in major risk factors, occurring despite some improvements in treatment uptake. CHD mortality rates fell by 17% in oPt and by 25% in Turkey, with risk factor changes accounting for around 46% and 30% of this reduction respectively. Increased uptake of community treatments (drug treatments for chronic angina, heart failure, hypertension and secondary prevention after a cardiac event) accounted for most of the remainder.
DISCUSSION: CHD death rates are rising in Tunisia and Syria, whilst oPt and Turkey demonstrate clear falls, reflecting improvements in major risk factors with contributions from medical treatments. However, smoking prevalence remains very high in men; obesity and diabetes levels are rising dramatically.
KEYWORDS: Cardiovascular mortality | Eastern Mediterranean | Model | Risk factor | Treatment | Trend