We present a data-driven and physics-informed algorithm for drilling accident forecasting. The core machine-learning algorithm uses the data from the drilling telemetry representing the time-series. We have developed a Bag-of-features representation of the time series that enables the algorithm to predict the probabilities of six types of drilling accidents in real-time. The machine-learning model is trained on the 125 past drilling accidents from 100 different Russian oil and gas wells. Validation shows that the model can forecast 70% of drilling accidents with a false positive rate equals to 40%. The model addresses partial prevention of the drilling accidents at the well construction. 

/pdf/forecasting-the-abnormal-events-at-well-drilling-with-2tpv6b50.pdf

Forecasting the abnormal events at well drilling with machine learning

Heart disease is contributing one of the leading reasons of death in the contemporary world. The three major danger signs for heart disease are smoking, high blood pressure and cholesterol, and 47% of all US citizens have at least one of these risk factors. In the field of clinical data analysis, predicting cardiovascular disease is a major difficulty. In this case, Machine learning (ML) can be important for taking decisions and predictions about heart disease based on personal key indicators (e.g., blood pressure, cholesterol level, smoking, diabetic status, obesity, stroke, alcohol drinking) of heart disease. In this paper, we proposed six machine learning models using survey data of over 400k US residents from the year 2020. The six machine learning models-Xgboost, Adaboost, Random Forest, Decision Tree, Logistic Regression, and Naïve Bayes have been compared in detail. Through the prediction model for heart disease, we achieved an improved performance level with an accuracy level of 91.57% for the prediction of heart diseases with the logistic regression model.

MLHeartDis:Can Machine Learning Techniques Enable to Predict Heart Diseases?

Cardiac disorders are one of the leading causes of mortality around the globe and early diagnosis of heart diseases can be beneficial for its mitigation. In this article, an artificial intelligence (AI) based device has been proposed, which allows for an automatic and real-time diagnosis of cardiac diseases based on deep learning techniques. The heart sound (phonocardiogram) signal is acquired by a customized designed stethoscope and the signal is processed before analysis using AI methods for the classification of four major cardiac diseases (Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, and Mitral Valve Prolapse). Two deep learning-based neural networks, one-dimensional (1-D) convolutional neural network (CNN) and spectrogram based 2-D-CNN models from the analysis of these signals has been integrated with a low-cost single-board processor to make a standalone device. All data processing is done in a single hardware setup and user interface is provided allowing the user to control the data accessibility and visibility to generate the diagnostic report. As a result, the developed device has demonstrated to be a valuable low-cost diagnostic tool for both medical professionals and personal usage at home. 

/pdf/ai-cardiocare-artificial-intelligence-based-device-for-2q900wag.pdf

AI-CardioCare: Artificial Intelligence Based Device for Cardiac Health Monitoring

Nowadays, digitalization in the healthcare organizations places great emphasis on technological advances in clinical healthcare providers. Traditional methods for measuring and evaluating outcomes for patients in forecasting and diagnosing chronic diseases are being substituted by techniques that capture the most significant insights from medical information by combining predictive modeling with a highly valuable application of machine learning. Heart disease is nowadays among the worst disorders in the world. Because the death rate from heart disease remained largely significant, more intensive efforts in preventive are required, such as enhancing the accuracy of a heart disease prediction system. Additionally, an early diagnosis supports in the appropriate diagnosis of the condition as well as the management of its symptoms. By creating forecasting analytics, Machine Learning (ML) techniques can be used to anticipate chronic diseases including kidneys and cardiac disorders. Hence, this analysis presents coronary heart disease prediction and classification utilizing Hybrid Machine Learning methods. In this approach the combination of Decision Tree (DT) and Ada Boosting algorithms is used as a hybrid ML algorithm to predict the CHD. This method's performance is determined by the performance metrics such as accuracy, True Positive Rate (TPR), and Specificity.

Coronary Heart Disease Prediction and Classification using Hybrid Machine Learning Algorithms

Cardiac disorders are one of the leading causes of mortality around the globe and early diagnosis of heart diseases can be beneficial for its mitigation. In this article, an artificial intelligence (AI) based device has been proposed, which allows for an automatic and real-time diagnosis of cardiac diseases based on deep learning techniques. The heart sound (phonocardiogram) signal is acquired by a customized designed stethoscope and the signal is processed before analysis using AI methods for the classification of four major cardiac diseases (Aortic Stenosis, Mitral Regurgitation, Mitral Stenosis, and Mitral Valve Prolapse). Two deep learning-based neural networks, one-dimensional (1-D) convolutional neural network (CNN) and spectrogram based 2-D-CNN models from the analysis of these signals has been integrated with a low-cost single-board processor to make a standalone device. All data processing is done in a single hardware setup and user interface is provided allowing the user to control the data accessibility and visibility to generate the diagnostic report. As a result, the developed device has demonstrated to be a valuable low-cost diagnostic tool for both medical professionals and personal usage at home.

In the present time deaths because of heart disease has become a significant issue roughly one individual kicks the bucket every moment because of heart disease. Machine learning includes man-made brainpower, and it is utilized in taking care of numerous issues in information science. One normal utilization of Machine learning is the expectation of a result dependent on existing information. The machine takes in designs from the current dataset, and afterward applies them to an obscure dataset so as to anticipate the result. Characterization is an amazing Machine learning strategy that is regularly utilized for forecast. Some order calculations anticipate with acceptable precision, while others show a constrained exactness. This paper explores a technique named outfit characterization, which is utilized for improving the exactness of frail calculations by consolidating different classifiers. Investigations with this apparatus were performed utilizing a heart disease dataset. The focal point of this paper isn’t just on expanding the exactness of frail order calculations, yet in addition on the execution of the calculation with a restorative dataset, to demonstrate its utility to anticipate infection at a beginning period. The consequences of the investigation show that group strategies, for example, stowing and boosting, are viable in improving the expectation precision of feeble classifiers, and display palatable execution in distinguishing danger of heart disease.

C Sanjana.

Papers

Improving the Accuracy in Prediction of Heart Disease using Machine Learning Algorithms