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HEART FAILURE PREDICTION
ISSN: 2582 - 9130
Publisher: author
HEART FAILURE PREDICTION
Indexed in
Technology and Engineering
ARTICLE-FACTOR
1.3
Article Basics Score: 2
Article Transparency Score: 2
Article Operation Score: 3
Article Articles Score: 2
Article Accessibility Score: 2
SUBMIT PAPER ASK QUESTION
International Category Code (ICC):
ICC-1802
Publisher: Krishma Publication
International Journal Address (IAA):
IAA.ZONE/2582384209130
eISSN
:
2582 - 9130
VALID
ISSN Validator
Abstract
Heart is one of the most important Part of the body. It helps to purify and circulate blood to all parts of the body. Most number of deaths in the world are due to heart diseases. Some symptoms like chest pain, faster heartbeat, discomfort in breathing is recorded. This data is analyzed on regular basis. In this review, an overview of the heart disease and its current procedures is firstly introduced. Furthermore, an in-depth analysis of the most relevant machine learning techniques available on the literature for heart disease prediction is briefly elaborated. The discussed machine learning algorithms are Decision Tree, SVM, ANN, Naive Bayes, Random Forest, KNN. The algorithms are compared on the basis of features. We are working on the algorithm with best accuracy. This will help the doctors to assist the heart problem easily. Keywords: Machine Learning, Prediction, Classification Technique, Decision Tree, Accuracy
