Canadian Journal of Medicine

Abstract

In clinical, sciences expectation of heart malady is one of the foremost troublesome
undertakings. Nowadays, coronary illness may be a significant reason for bleakness and
mortality in present-day society. Coronary illness could be a term that doles intent on countless
ailments identified with the heart. Clinical determination is incredibly a big, however entangled
errand that must be performed precisely, effectively, and unequivocally. Although huge
advancement has been imagined within the finding and treatment of coronary illness, further
examination is required. The accessibility of enormous measures of clinical information
prompts the requirement for amazing information examination instruments to get rid
of valuable information. Coronary illness determination is one in all the applications where
information mining and AI instruments have demonstrated victories. This study used the
machine learning algorithms KNN, Naïve Bayes, Random forest, Logistic regression, Support
vector machine, J48, and Decision tree by WEKA software to spot which method provides
maximum performance and accuracy. Using these algorithms with WEKA software, we made
an ensemble (Vote) hybrid model by combining individual methods. Our research aims to
access the effectiveness of various machine learning algorithms to diagnose the center disease
and find the feasible algorithm, which is that the best for a heart condition