Heart Diseases Prediction Using Machine Learning
Shaik Wajid, Gundla Sowmya, Maruthi Sravan Reddy , Mohammed Farooq
Machine learning, Logistic regression, Heart disease, Support vector machine, accuracy
Heart Disease Prediction is one of the most complicated tasks in medical field. In the modern era, approximately one person dies per minute due to heart disease. Data science plays a crucial role in processing huge amount of data in the field of healthcare. As heart disease prediction is a complex task, there is a need to automate the prediction process to avoid risks associated with it and alert the patient well in advance. This paper makes use of heart disease dataset available in UCI machine learning repository. The proposed work predicts the chances of heart disease and classifies patient's risk level by implementing different data mining techniques such as Naive Bayes, Decision Tree, Logistic Regression and Random Forest. Thus, this paper presents a comparative study by analysing the performance of different machine learning algorithms. The trial results verify that Random Forest algorithm has achieved the highest accuracy of 90.16% compared to other ML algorithms implemented.
Article Details
Unique Paper ID: 162575

Publication Volume & Issue: Volume 10, Issue 10

Page(s): 500 - 505
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