Prediction of Heart Disease Using Machine Learning
Rachit Misra, Prashuk Jain, Pulkit Gupta
Decision Tree, Naive Bayes, Logistic Regression, Random Forest, heart condition Prediction
Heart Disease prediction is one among the foremost complicated tasks in medical field. In the era , approximately one person dies per minute thanks to heart condition . Data science plays an important role in processing huge amount of knowledge within the field of healthcare. As heart condition prediction may be a complex task, there's a requirement to automate the prediction process to avoid risks related to it and alert the patient well beforehand . This paper makes use of heart condition dataset available in UCI machine learning repository. The proposed work predicts the probabilities of heart condition and classifies patient's risk level by implementing different data processing techniques like Naive Bayes, Decision Tree, Logistic Regression and Random Forest. Thus, this paper presents a comparative study by analysing the performance of various machine learning algorithms. The trial result verifies that Random Forest algorithm has achieved the highest accuracy of 90.16% compared to other ML algorithms implemented.
Article Details
Unique Paper ID: 152152

Publication Volume & Issue: Volume 8, Issue 2

Page(s): 643 - 646
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