Heart Disease Risk Prediction: A Comparison of Machine Learning Techniques
Keywords:
Classification Algorithm, Decision Making, Machine Learning, Risk PredictionAbstract
Healthcare services have once again demonstrated their worldwide importance under the pandemic conditions. Under the leadership of Industry (4.0), data mining continues to develop in the field of health. Data mining prediction tool act a critical role in healthcare. Heart disease is the most dangerous noncommunicable disease in the world. To predict heart disease, a variety of data mining techniques are used. The study's goal is to use classification algorithms to predict the occurrence of heart disease in an individual. In the study, a dataset consisting of 14 variables belonging to 303 patients accessed from the Kaggle site was used. 75% of the dataset is split into training sets and 25% into test sets in order to train and test the model. Classification performances were compared using K-Nearest Neighborhood (KNN), Random Forest (RF) and Decision Tree (DT) algorithms. As a result of the study, it was determined that the classification accuracy of KNN, RF and DT algorithms was 88.16%, 89.47% and 84.21%, respectively.
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