Prediction of Heart Disease Using Machine Learning

By A. Gavhane, Gouthami Kokkula, Isha Pandya, P. K. Devadkar. 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2018

310 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Health Information Management

View Paper: https://www.semanticscholar.org/paper/828d61fd204bebf70a506ecc0720e4287bec4fd0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Health Information Management

1. Gautam Srivastava
2. Senthilkumar Mohan
3. Chandrasegar Thirumalai

Citations in the corpus

• Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques (2021, methodology): “The implemented model is written in Jupiter notebook’s Python programming language using simple libraries like Panda [56], Pyplot [57] and Scikit-learn [58].”
• Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques (2019, methodology): “The severity of the disease is classified based on various methods like K -Nearest Neighbor Algorithm (KNN), Decision Trees (DT), Genetic algorithm (GA), and Naive Bayes (NB) [11], [13].”