Artificial Intelligence and Classification Algorithms in Heart Disease Data: Modern Approaches and Performance Comparison

This study presents a data mining application aimed at investigating the prediction performance of classification algorithms on heart disease datasets. In this research, the likelihood of individuals having heart disease based on specific features was evaluated using various classification algorithms. The dataset used was created by John Moore's University in Liverpool, UK, and was last updated on June 6, 2020. The dataset consists of 1190 samples with 11 features. The study utilised several classification algorithms, including regression, k- nearest neighbours (KNN), Naive Bayes, random forest, decision trees, and support vector machines (SVM). All algorithms were implemented using the Python programming language and the Jupyter Notebook environment, and their classification performances were compared. The evaluation of success was based on metrics such as accuracy, sensitivity, specificity, and F1 score. According to the results, KNN, support vector machines, and random forest algorithms achieved the highest performance with an accuracy rate of 86.79%, outperforming the other algorithms. This study highlights the potential of classification algorithms in the early diagnosis of heart disease, emphasising the significance of artificial intelligence and data mining applications in the healthcare field.