Arabasadi, Z., Alizadehsani, R., Roshanzamir, M., Moosaei, H., Yarifard, A.A., 2017.An Automated Diagnostic System for Heart Disease Prediction Based on x2 Statistical Model and Optimally Configured Deep Neural Network. An optimized stacked support vector machines based expert system for the effective prediction of heart failure. Classification of individuals at risk of heart disease using machine learning. It was observed that the measurements obtained were higher than the results obtained in similar studies conducted in the literature in recent years. The highest accuracy value was obtained as 98.46% in the classification using Random Forest and InstanceHardnessThreshold undersampling technique. The result of each classification process is reported with 5 different parameters: accuracy, precision, recall, F1 score, and AUC. For each of 8 different machine learning techniques, namely Naive Bayes, Decision Trees, Support Vector Machine, K Nearest Neighbor, Logistic Regression, Random Forest, AdaBoost, and CatBoost, in addition to no-sampling classification, 8 different methods from oversampling and undersampling techniques were used to make a total of 72 classification processes were carried out. In order to increase the classification success, resampling techniques were used to ensure the class balance of the dataset.
The study was carried out on the UCI heart disease dataset, which is widely used in the literature. In this study, classifications were made using various machine learning techniques for the early diagnosis of heart diseases. Machine learning, an artificial intelligence discipline, provides experts with successful decision support solutions in diagnosing new cases based on known data. The difficulty in distinguishing the symptoms of heart disease and the fact that most heart patients are not aware of the symptoms until the moment of crisis make the diagnosis of the disease difficult. Heart diseases are common worldwide and cause one-third of global deaths.
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