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Abstract

Heart disease is the leading cause of death indeveloped countries, as it causes many deaths annually. Despite the availability of effective treatments, heartdisease remains a significant challenge to public health, so early detection is essential in enhancing patient outcomesand reducing mortality.Artificial intelligence seeks to help physicians make the right decisions about a patient's health condition. In this regard, the authors decided to utilize machine learning techniques (k-nearest neighbor, decision tree, linear regression, support vector machine, naïve bayes, multilayer perceptron, random forest) to contribute to the classification of the heart disease dataset, where it is determined whether a person is suffering or not. After that, the execution of all techniques will be measured, andtheaccuracy of each technique will be compared to determine the most suitable performer.The public dataset is organized from the UC Irvine machine learning repository and have significantly different characteristics. The dataset will be divided such that 80% of the data is designated for training and 20% is designated for testing. This article concluded that the adequate performance is for the multilayer perceptron technique, as it gained an accuracy of more than 88%, while the poor performance is for the decision tree technique, as it gained an accuracy of more than 79%.

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