[PDF] Top 20 A data-driven approach to predicting diabetes and cardiovascular disease with machine learning
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A data-driven approach to predicting diabetes and cardiovascular disease with machine learning
... the data (based on laboratory data), multiple machine learning models (logistic regression, support vector machines, random forest, and gradient boosting) were evaluated on their ... See full document
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Machine Learning With Factor Scoring To Predict Diabetes Risk Level In Bangladesh
... with diabetes risk prediction based on local ...where machine learning and data analysis applied to predict the diabetes risk level or used different machine learning ... See full document
5
Machine learning methodologies versus cardiovascular risk scores, in predicting disease risk
... population data, alternatively to the estab- lished risk prediction ...three machine-learning classifiers were compared against an estimation tool for CVD risk prediction, as well as against actual ... See full document
11
Big Data Processing System for Diabetes Prediction using Machine Learning Technique
... – Diabetes is one of the threatening diseases to the entire mankind, though it is not ...for diabetes prediction, big data based diabetes prediction is quite ...for predicting ... See full document
6
Predicting Adverse Outcomes in End Stage Renal Disease: Machine Learning Applied to the United States Renal Data System
... renal disease (ESRD) have an extremely high extra renal morbidity and age adjusted mortality compared with the general population in the United ...Renal Data System (USRDS); 9 a number of qualitative and ... See full document
15
Survey of Machine Learning Algorithms for Disease Diagnostic
... ments. Machine learning is important in Computer Aided ...involves learning from ...and machine learning promise the improved accuracy of perception and diagnosis of ...bio-medical ... See full document
16
A Machine Learning Approach to Predicting Coverage in Random Wireless Networks
... Abstract —There is a rich literature on the prediction of coverage in random wireless networks using stochas- tic geometry. Though valuable, the existing stochastic geometry-based analytical expressions for coverage are ... See full document
7
Cardiovascular Disease Prediction from Electrocardiogram by Using Machine Learning
... Abstract—Cardiovascular disease (CVD) is the leading cause of deaths ...costly. Machine learn- ing (ML) techniques allow an accurate prediction by utilizing the complex in- teractions among relevant ... See full document
15
Cardiovascular Disease Prediction using Machine Learning Techniques
... on data mining, machine learning plays an important role in predicting heart ...medical data to prevent heart diseases, mining is used to identify patterns which are hidden and ... See full document
9
Predicting Diabetes Disease using Effective Classification Techniques
... the Machine Learning Techniques for Diabetes ...Vector Machine), etc.) used to identify diabetes and data preprocessing ... See full document
6
Data Driven Approach for Eye Disease Classification with Machine Learning
... More than one measure are usually considered when classifiers are compared because one measure, e.g., accuracy, considers only correct predictions, whereas others, e.g., RMSE, consider only false predictions. However, ... See full document
17
A Review on Heart Disease Prediction using Machine Learning and Data Analytics Approach
... work. Data analytics is useful for prediction from more information and it helps medical centre to predict of various ...related data is maintained on monthly basis. The stored data can be useful for ... See full document
6
Predicting Malnutrition Disease Using Various Machine Learning Algorithms
... the data in Ms-excel 10 text file format. In addition to the native ARFF data file format, WEKA has ability to read as ...the data set consist of the parameters ... See full document
6
Diabetes mellitus and apoptosis:inflammatory cells
... Macrophage apoptosis occurs during all stages of ath- erosclerosis, and with improvements in the development of mouse models it has been possible to explore the functional consequences at each stage of the disease ... See full document
16
Deep Learning For Anticipation Of Cardiovascular Disease: A Practical Approach
... Cardiac disease [1] typically permits cases involving shrinking or occluding blood vessels which can result in cardiac attack, angina or ...cardiac disease. Cardiac disease side effects might vary ... See full document
6
Prediction Of Misclassification Data Based On Cognitive Computation Approach (CCA)
... predictive data analysis technique to classify the ...Incomplete data has proved to be the rule rather than an exception in real-world data mining ...successful data mining process, since ... See full document
8
Epidemiology of cataract surgery in Noor Ophthalmology Clinic of Birjand in 2015-2016
... common disease across the world and the most common cause of vision loss and ...the disease, the most important of which is ...history, diabetes, location, separate left and right eye, family, job, ... See full document
5
Metabolic biomarkers for predicting cardiovascular disease
... While the definition of the term has changed over the years, so too have the statistics which are used to quantify the utility of each biomarker. The first statistic used is the receiver-operating characteristic (ROC). ... See full document
9
<p>Prediction of cardiovascular outcomes with machine learning techniques: application to the Cardiovascular Outcomes in Renal Atherosclerotic Lesions (CORAL) study</p>
... The data were cleaned by exclud- ing variables with large numbers of missing values ( > 40% missing ...numeric data had the average value placed into missing value categories ( < 20% missing ... See full document
10
Index Terms- Big data analytics, Machine Learning, Healthcare, Disease Detection, Medical Data Analysis.
... big data, it is used in many fields, which means it is extremely large data sets that it holds and it is analyzed the direct patterns, logical trends and associations, it specially related to human oriented ... See full document
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