Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare

Abstract

Heart disease is one of the complex
diseases and globally many people suffered
from this disease. On time and efficient
identification of heart disease plays a key role
in healthcare, particularly in the field of
cardiology. In this article, we proposed an
efficient and accurate system to diagnosis
heart disease and the system is based on
machine learning techniques. The system is
developed based on classification algorithms
includes Support vector machine, Logistic
regression, Artificial neural network, Knearest neighbor, Naïve bays, and Decision
tree while standard features selection
algorithms have been used such as Relief,
Minimal redundancy maximal relevance,
Least absolute shrinkage selection operator
and Local learning for removing irrelevant
and redundant features. We also proposed
novel fast conditional mutual information
feature selection algorithm to solve feature
selection problem. The features selection
algorithms are used for features selection to
increase the classification accuracy and
reduce the execution time of classification
system. Furthermore, the leave one subject
out cross-validation method has been used
for learning the best practices of model
assessment and for hyper parameter tuning.
The performance measuring metrics are used
for assessment of the performances of the
classifiers. The performances of the
classifiers have been checked on the selected
features as selected by features selection
algorithms. The experimental results show
that the proposed feature selection algorithm
(FCMIM) is feasible with classifier support