Data Mining for Cardiovascular Disease Prediction

2021 ◽  
Vol 45 (1) ◽  
Author(s):  
Bárbara Martins ◽  
Diana Ferreira ◽  
Cristiana Neto ◽  
António Abelha ◽  
José Machado
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Disease Prediction

Cardiovascular Disease Prediction Using Data Mining Techniques

2019 ◽  
pp. 246-264
Author(s):  
Kalyani Kadam ◽  
Pooja Vinayak Kamat ◽  
Amita P. Malav
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Big Data ◽  
Cardiovascular Diseases ◽  
Primary Objective ◽  
Disease Prediction ◽  
Data Mining Techniques ◽  
Data Architecture ◽  
Life Threatening ◽  
Using Data

Cardiovascular diseases (CVDs) have turned out to be one of the life-threatening diseases in recent times. The key to effectively managing this is to analyze a huge amount of datasets and effectively mine it to predict and further prevent heart-related diseases. The primary objective of this chapter is to understand and survey various information mining strategies to efficiently determine occurrence of CVDs and also propose a big data architecture for the same. The authors make use of Apache Spark for the implementation.

Cardiovascular Disease Prediction Using Data Mining Techniques

2019 ◽  
pp. 159-179
Author(s):  
Kalyani Kadam ◽  
Pooja Vinayak Kamat ◽  
Amita P. Malav
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Big Data ◽  
Cardiovascular Diseases ◽  
Primary Objective ◽  
Disease Prediction ◽  
Data Mining Techniques ◽  
Data Architecture ◽  
Life Threatening ◽  
Using Data

Cardiovascular diseases (CVDs) have turned out to be one of the life-threatening diseases in recent times. The key to effectively managing this is to analyze a huge amount of datasets and effectively mine it to predict and further prevent heart-related diseases. The primary objective of this chapter is to understand and survey various information mining strategies to efficiently determine occurrence of CVDs and also propose a big data architecture for the same. The authors make use of Apache Spark for the implementation.

scholarly journals Heart disease prediction using data mining

2021 ◽  
Vol 1913 (1) ◽  
pp. 012099
Author(s):  
R Fadnavis ◽  
K Dhore ◽  
D Gupta ◽  
J Waghmare ◽  
D Kosankar
Keyword(s):  
Data Mining ◽  
Heart Disease ◽  
Disease Prediction ◽  
Using Data

scholarly journals Cardiovascular disease prediction: do pulmonary disease-related chest CT features have added value?

2015 ◽  
Vol 25 (6) ◽  
pp. 1646-1654
Author(s):  
Pushpa M. Jairam ◽  
◽  
Pim A. de Jong ◽  
Willem P. Th. M. Mali ◽  
Ivana Isgum ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Pulmonary Disease ◽  
Chest Ct ◽  
Added Value ◽  
Disease Prediction ◽  
Ct Features

Using Stratified Sample and Grid Search to Improve Disease Prediction Accuracy of SVM

2013 ◽  
Vol 295-298 ◽  
pp. 644-647 ◽  
Author(s):  
Yu Kai Yao ◽  
Hong Mei Cui ◽  
Ming Wei Len ◽  
Xiao Yun Chen
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Classification Accuracy ◽  
Prediction Accuracy ◽  
Support Vector ◽  
Disease Prediction ◽  
Data Mining Algorithm ◽  
Grid Search ◽  
Mining Algorithm ◽  
Stratified Sample

SVM (Support Vector Machine) is a powerful data mining algorithm, and is mainly used to finish classification or regression tasks. In this literature, SVM is used to conduct disease prediction. We focus on integrating with stratified sample and grid search technology to improve the classification accuracy of SVM, thus, we propose an improved algorithm named SGSVM: Stratified sample and Grid search based SVM. To testify the performance of SGSVM, heart-disease data from UCI are used in our experiment, and the results show SGSVM has obvious improvement in classification accuracy, and this is very valuable especially in disease prediction.

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