Abstract:
Machine learning, Data mining are fundamental in health care also in health care
information and identification are essential. Machine learning approaches has recently been
utilized to detect and forecast a variety of major health hazards, including diabetes
prediction, brain tumor detection, renal problem prediction, and Covid-19 identification,
among others. The part of heart is precious organ of our body and if it has any problem then
the impact is more dangerous to our body. According to the Centers for Disorder Control
and Prevention (CDC) Trusted Source, heart disorder is the leading cause of death
worldwide. We use a few attributes to check our heart disorder analysis, and this attribute
is one of the most common causes of heart disorder. As a consequence, 6 machine learning
classifiers are employed to evaluate the data using Google Collaboratory: Naive Bayes
(NB), Logistic Regression (LG), K Nearest Neighbor, Bagging, Decision Tree (DT), and
Random Forest (RF). Using the Seaborn distplot, we extract all attributes' features. Here,
Applying Random Forest Algorithm (RF) we get the best accuracy, which is 99.18 %. We
have the biggest value of the ROC (receiver operating characteristic) curve of any other
algorithm.
