Heart Disease Prediction Using Machine Learning
Author(s):
Sashank Yadav, Aman Singh, Veena Jadhav, Dr. Rohini Jadhav
Keywords:
Naive Bayes, Decision Tree, Logistic Regression, KNN (K-Nearest Neighbors), SVM (Support Vector Machine), Gradient Boosting, Random Forest [3], Algorithm, Machine Learning
Abstract
Lately, predicting any cardiac disease is the most complex tasks in the health care sector. At present, nearby one person per minute dies of a coronary attack [1]. The untimely occurrence turns into life taking scenario. Data science as a domain has an important role in gathering insights from huge amounts of data in the health care sector as predicting any heart disease is a real complex task, there is a need to automate the prediction process in order to avoid risks associated with and alert the patient in-advance. This paper uses the heart disease dataset from the UCI Machine Learning repository [2] [1]. The work here predicts the possibility of heart disease by using 7 machine learning algorithms such as the Naive Bayes, Decision Tree, Logistic Regression, KNN (K-Nearest Neighbors), SVM (Support Vector Machine), Gradient Boosting and Random Forest algorithms [3]. Therefore, this paper brings up a comparison of performing measures between different machine learning algorithms used in the proposed work. The results acquired from the classification report confirms that the KNN (K-Nearest Neighbors) algorithm achieved a very high accuracy of 85.18% compared to other ML algorithms used. This Algorithmic model is then serialized into a byte stream as a pickle file(pkl) which is unpickled in the web application developed via Flask micro web framework. The application performs predictions over the user inputs via the HTML template and returns the prediction.
Article Details
Unique Paper ID: 156060

Publication Volume & Issue: Volume 9, Issue 2

Page(s): 761 - 765
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