AI Summary & Key Takeaways
AI InsightAlso, certain rules regarding the disease to be used in preventive medicine practices were obtained with the proposed model.
📌 3 Key Takeaways
Abstract
Aim: This study aims to predict mortality status by heart failure and to determine the related factors by applying the relational classification method, one of the data mining methods, on the open-access heart failure data set. Materials and Methods: In this study, the associative classification model has been applied to the open-access data set named “Heart Failure Prediction”. The performance of the model was evaluated by accuracy, balanced accuracy, sensitivity, selectivity, positive predictive value, negative predictive value, and F1-score. Results: Accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score values obtained from the model were 0.866, 0.819, 0.688, 0.951, 0.868, 0.865 and 0.767 respectively. Conclusion: The findings obtained from this study showed that successful results were obtained in the study performed with the associative classification model on the heart failure data set. Also, certain rules regarding the disease to be used in preventive medicine practices were obtained with the proposed model.