Proceedings ArticleDOI
Improving the Accuracy in Prediction of Heart Disease using Machine Learning Algorithms
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TLDR
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.Abstract:
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.read more
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Forecasting the abnormal events at well drilling with machine learning
TL;DR: In this article , the authors presented a data-driven and physics-informed algorithm for drilling accident forecasting, which used the data from the drilling telemetry representing the time-series and 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.
Proceedings ArticleDOI
MLHeartDis:Can Machine Learning Techniques Enable to Predict Heart Diseases?
TL;DR: In this paper , six machine learning models (Xgboost, Adaboost, Random Forest, Decision Tree, Logistic Regression, and Naïve Bayes) have been compared in detail.
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AI-CardioCare: Artificial Intelligence Based Device for Cardiac Health Monitoring
TL;DR: In this paper , 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, where 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.
Proceedings ArticleDOI
Coronary Heart Disease Prediction and Classification using Hybrid Machine Learning Algorithms
TL;DR: In this paper , the combination of Decision Tree (DT) and Ada Boosting algorithms is used as a hybrid ML algorithm to predict the coronary heart disease (CHD) using the performance metrics such as accuracy, True Positive Rate (TPR), and Specificity.
Journal ArticleDOI
AI-CardioCare: Artificial Intelligence Based Device for Cardiac Health Monitoring
TL;DR: 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, where 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.
References
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Book
The Atlas of Heart Disease and Stroke
TL;DR: The atlas of heart diseases and stroke as mentioned in this paper is a comprehensive atlas for heart disease and stroke, which includes the following categories: heart disease, stroke, cancer, and stroke.
Journal ArticleDOI
The atlas of heart disease and stroke
TL;DR: The atlas of heart diseases and stroke is described as "the most comprehensive and comprehensive manual on heart disease and stroke ever written".
Journal ArticleDOI
Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction
TL;DR: A survey of current techniques of knowledge discovery in databases using data mining techniques that are in use in today’s medical research particularly in Heart Disease Prediction reveals that Decision Tree outperforms and some time Bayesian classification is having similar accuracy as of decision tree but other predictive methods are not performing well.
Journal ArticleDOI
Identification of significant features and data mining techniques in predicting heart disease
TL;DR: Experimental 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.
Deaths : leading causes for 2006
TL;DR: Differences in the rankings are evident by age, sex, race, and Hispanic origin, and important variations in the leading causes of infant death are noted for the neonatal and postneonatal periods.