Analysis and Prediction of Heart Disease Using Machine Learning and Data Mining Techniques

Abstract

In clinical, sciences expectation of heart malady is one of the foremost troublesomeundertakings. Nowadays, coronary illness may be a significant reason for bleakness andmortality in present-day society. Coronary illness could be a term that doles intent on countlessailments identified with the heart. Clinical determination is incredibly a big, however entanglederrand that must be performed precisely, effectively, and unequivocally. Although hugeadvancement has been imagined within the finding and treatment of coronary illness, furtherexamination is required. The accessibility of enormous measures of clinical informationprompts the requirement for amazing information examination instruments to get ridof valuable information. Coronary illness determination is one in all the applications whereinformation mining and AI instruments have demonstrated victories. This study used themachine learning algorithms KNN, Naïve Bayes, Random forest, Logistic regression, Supportvector machine, J48, and Decision tree by WEKA software to spot which method providesmaximum performance and accuracy. Using these algorithms with WEKA software, we madean ensemble (Vote) hybrid model by combining individual methods. Our research aims toaccess the effectiveness of various machine learning algorithms to diagnose the center diseaseand find the feasible algorithm, which is that the best for a heart condition