Prognosis of Heart Disease using Data Mining Techniques: A Comprehensive Survey

Naik, Ankita; Naik, Nitesh

doi:10.5120/ijca2018917765

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2019

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[3] In world, Medical theorist facing a problem of predicting the heart related issues before they happen. Different Techniques are employed in reducing medical cost related problems and early prediction of disease.Naik.et.al provides shallow analysis and understanding of various prediction models based on data mining from last seven years [4]. The authors presented the comparison between each model given by various authors in terms of accuracy.…”

Section: Introductionmentioning

confidence: 99%

Heart Disease Risk Predictor using Support Vector Machine

Saharan¹,

Mishra²,

Garg³

et al. 2019

IJRTE

View full text Add to dashboard Cite

Nearly 17.5 million deaths occur due to cardiovascular diseases throughout the world. If we could create such a mechanism or system that could tell people about their heart condition based on their medical history and warn them of any risk than it could be of huge help. In our work, we will use machine learning algorithms to forecast the heart disease risk factor for a person depending upon some attributes in their medical history. The data mining technique Naive Bayes, Decision tree, Support Vector Machine, and Logistic Regression is analyzed on the Heart disease database. The accuracy of different algorithms is measured and then the algorithms are compared.

show abstract

Section: Introductionmentioning

confidence: 99%