Heart Disease Prediction using Machine Learning Techniques
Raparthi Yaswanth1, Y. Md. Riyazuddin2

1Raparthi Yaswanth*, MTech (Data Science) in the Department of CSE at GITAM University, Hyderabad.
2Dr. Y. Md. Riyazuddin, Assistant Professor, Department of CSE at GITAM University, Hyderabad.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 22, 2020. | Manuscript published on March 10, 2020. | PP: 1456-1460 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2862039520/2020©BEIESP | DOI: 10.35940/ijitee.E2862.039520
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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: Recent advancement of technology allows the automation of things to be done using machine learning techniques. These machine learning techniques can also be used for detecting or predicting the heart disease in the early phase. The health care industry produces a huge amount of data which is in unstructured manner that cannot be understood by a machine. Due to development of modern technology, health care industries also managing the data in a structured manner which can be understood by machine learning technology. In this environment if we use machine learning algorithms for prediction of heart disease, then there is a chance to detect the heart disease status in the early phase and to alert patient to get a better treatment to cure that disease. This paper implements seven supervised learning algorithms which are KNN, Decision Tree, Naive Bayes, Logistic Regression, Random Forest, Support Vector Machine and Neural Networks for heart disease prediction. This paper generates algorithm performance metrics like Accuracy, Precision, Recall, F-score and ROC values for how the system was predicting accurately. In this paper among those seven algorithms, Neural Networks gave best accuracy as 92.30% and this system provides experimental results for how the model is accurate for heart disease prediction.
Keywords: Heart disease prediction, Decision Tree, Naive Bayes, Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Neural Networks.

Scope of the Article: Machine Learning