Heart Disease Prediction using Machine Learning Models
Ruban1, Vivek2,  Krithi3

1Dr. Ruban S, Assistant Professor HOD, Department of Software Technology, St Aloysius College, Mangalore (Karnataka), India.
2Vivek, Student, Department of Software Technology, St Aloysius College, Mangalore (Karnataka), India.
3Krithi, Student, Department of Software Technology, St Aloysius College, Mangalore (Karnataka), India.
Manuscript received on 13 February 2020 | Revised Manuscript received on 20 February 2020 | Manuscript Published on 28 February 2020 | PP: 53-57 | Volume-8 Issue-5S February 2020 | Retrieval Number: E10120285S20/2020©BEIESP | DOI: 10.35940/ijrte.E1013.0285S20
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Healthcare has become one of the most important concerns in the world. The cases of heart disease are increasing on a rapid scale among the people especially among the young generation. We can save the lives of the people if we could detect the heart disease on/before time, by getting them treated. In this matter artificial intelligence can be of a great help. Here we have collected a data set and then we have built a prediction model to detect heart disease based on the various algorithms that are available for machine learning.we have used Logistic regression, K-NN, SVM, Decision Tree, Random Forest with the accuracy values of K-Neighbors Classifier (0.956194%), Support Vector Machine (0.9561945%), Decision Tree (0.91050%), Random Forest Classifier (0.95404%) and Logistic Regression (0.95592%). The best value given by the Machine Learning model is by Logistic regression followed by K-NN.
Keywords: Heart Disease, Predictive Model, Machine Learning, Artificial Intelligence.
Scope of the Article: Machine Learning