[PDF] Top 20 Can machine learning improve cardiovascular risk prediction using routine clinical data
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Can machine learning improve cardiovascular risk prediction using routine clinical data
... Globally, cardiovascular disease (CVD) is the leading cause of morbidity and ...CVD risk assessment, such as that recommended by the American Heart Association/American College of Cardiology (ACC/AHA), ... See full document
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Predicting early risk of chronic kidney disease in cats using routine clinical laboratory tests and machine learning
... CKD prediction model developed in this study brings several advantages for veterinary ...test data currently available for a particular ...these clinical measurements (Figure 5), most likely due to ... See full document
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Cardiovascular Disease Prediction from Electrocardiogram by Using Machine Learning
... CVD prediction are usually invasive and costly. Machine learn- ing (ML) techniques allow an accurate prediction by utilizing the complex in- teractions among relevant risk ...highest ... See full document
15
Cancer Prediction and Prognosis Using Machine Learning Techniques
... of clinical data in the research registry influenced the performance of their ...their clinical knowledge to select 14 out of 193 variables may have resulted in significant bias and thus giving no ... See full document
5
Health Risk Prediction by Machine Learning over Data Analytics
... disease risk and taking action at the earliest ...Big Data of similar records in order to predict future diseases ...Disease Risk Prediction (CNN-MDRP) algorithm using structured and ... See full document
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Bridging a translational gap: using machine learning to improve the prediction of PTSD
... In a previous study [23], we evaluated the ability of ML-based feature-selection algorithm to extract one set of early risk indicators. We also compared various ML classification algorithms and evaluated ... See full document
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Cardiovascular Disease Prediction using Machine Learning Techniques
... of risk of heart diseases will mitigate the situation to a great ...This can be achieved by automating the prediction of heart diseases by saving time and ...on data mining, machine ... See full document
9
A Study On Prediction Of Health Care Data Using Machine Learning
... in prediction. EHR (Electronic-Health Record) contains health-related data of many ...huge data collections and to predict the disease based on the given ...and prediction processes are taken ... See full document
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<p>Prediction of cardiovascular outcomes with machine learning techniques: application to the Cardiovascular Outcomes in Renal Atherosclerotic Lesions (CORAL) study</p>
... the data. She, along with JIS wrote the R code used for machine learning and analyzed the CORAL data set with these ...the data. She prepared and summarized data for subsequent ... See full document
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Cardiovascular Disease Prediction Using Data Mining Techniques: A Review
... that cardiovascular diseases have high mortality rate and high risk to cause various ...for cardiovascular diseases are behavioural and food habits like tobacco intake, unhealthy diet and obesity, ... See full document
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Index Terms- Big data analytics, Machine Learning, Healthcare, Disease Detection, Medical Data Analysis.
... Disease prediction in big data healthcare using extended ...structured data (extracted useful features), the unstructured data is use the CNN technique, so automatically selects the ... See full document
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Machine learning with sparse nutrition data to improve cardiovascular mortality risk prediction in the USA using nationally randomly sampled data
... to improve predic- tion of CVD ...CVD risk factors of total cholesterol (mg/dL), high- density lipoprotein (HDL) cholesterol (mg/dL), systolic blood pressure (mm Hg), blood pres- sure treatment status ... See full document
9
Can Machine Learning Improve Recession Prediction Accuracy?
... of using statistical data mining is that important connections between different sectors are often unknown to ...Statistical machine learning can help an analyst identify those obscure ... See full document
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A Study of Classification Techniques of Data Mining Techniques in Health Related Research
... Vector Machine) classification: SVM was invented by Boser, Guyon and ...vector machine deals with pattern classification ...patterns can be easily distinguishable and non-linear patterns are not ... See full document
8
Real-Time Clustering For Big Data Streams
... of data in a distributed fashion that can be run on commodity resources, thus scaling the processing power to hundreds or thousands of ...processing data in streaming where disk I/O operations are ... See full document
7
Predictive Modeling for Land Suitability Assessment for Cassava Cultivation
... of machine learning algorithms to discover knowledge and important relationships in a dataset is a promising trend in recent technological ...advancement. Machine learning has been gaining ... See full document
11
The discovery BPD (D-BPD) program: study protocol of a prospective translational multicenter collaborative study to investigate determinants of chronic lung disease in very low birth weight infants
... contribute to a complex trait is a major challenge [21]. Furthermore, susceptibility genes interact with multiple environmental exposures or stimuli related to the eti- ology of a disease. In order to better define the ... See full document
10
Artificial intelligence on the identification of risk groups for osteoporosis, a general review
... at risk of osteoporosis or fractures resulting from this disease (see Table ...for machine learning ...this learning to be successful involves much more than choosing an algorithm and execut- ... See full document
17
A Survey on Disease Prediction by Machine Learning Using Big Data Analytics
... amount data over globe. This data Is in the form of hundreds of terabytes, ...Healthcare data is divided into Different subfields of data according to Patients diseases and treatments on ... See full document
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Can routine clinical data identify older patients at risk of poor healthcare outcomes on admission to hospital?
... A total of five focus groups and three interviews were conducted with 22 staff participants across the five study wards with different sex, professional bands, and years of experience. The sample comprised: seven nurses, ... See full document
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