Heart Disease Prediction With Machine Learning

Abstract

Heart disease, alternatively known as
cardiovascular disease, encases various conditions that
impact the heart and is the primary basis of death
worldwide over the span of the past few decades. It
associates many risk factors in heart disease and a need
of the time to get accurate, reliable, and sensible
approaches to make an early diagnosis to achieve
prompt management of the disease. Data mining is a
commonly used technique for processing enormous
data in the healthcare domain. Researchers apply
several data mining and machine learning techniques
to analyse huge complex medical data, helping
healthcare professionals to predict heart disease. This
research paper presents various attributes related to
heart disease, and the model on basis of supervised
learning algorithms as Naïve Bayes, decision tree, Knearest
neighbor, and random forest algorithm. It uses
the existing dataset from the Cleveland database of UCI
repository of heart disease patients. The dataset
comprises 303 instances and 76 attributes. Of these 76
attributes, only 14 attributes are considered for testing,
important to substantiate the performance of different
algorithms. This research paper aims to envision the
probability of developing heart disease in the patients.
The results portray that the highest accuracy score is
achieved with K-nearest neighbor