Heart disease prediction using machine learning techniques : a survey
By V. Ramalingam, A. Dandapath, M. Raja. 2018
296 citations recorded by the source corpus.
Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques andanalyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.
Subfields: Health Information Management
View Paper: https://www.semanticscholar.org/paper/fd328a4c3f414e87c64eafbd5b3625042adb4b78
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
Citations in the corpus
- Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques (2021, methodology): “In medical data mining, different classification algorithms are used to predict the CVD in patients and death predictions due to the heart attack [14].”