چكيده به لاتين
Nowadays, large amounts of medical data are generated and stored by medical equipment in hospitals and medical centers. In order to predict the condition of patients, analysis of this information is considered necessary and important. Today, predicting the status of heart disease is one of the most important issues in the healthcare industry. The present study was conducted to explore potential patterns between risk factors for heart disease and psychiatric disorders in Iranian children and adolescents using data mining techniques. Data were collected from children and adolescents in Yazd province using medical tests and the design of related questionnaires. The characteristics used in this study are demographic characteristics, psychiatric disorders, parental disorders, lifestyle, social capital, and characteristics related to heart disease risk factors. In this research, a comprehensive statistical analysis of the studied features has been performed and the existing patterns among them have been presented. The imbalance of data leads to poor predictions, so the SMOTE algorithm is used to balance the data. This research also uses two methods of feature selection such as feature importance and methods based on meta-heuristic algorithms, including genetic algorithm, particle swarm optimization, and ant colony to identify important features. So as to validate the performance of the implemented models, this research has used the ten-fold cross-validation. It should be noted that in order to predict the blood pressure of patients, ensemble learning models such as random forest and various gradient boosting models have been used. The results show that the combination of meta-heuristic algorithms for selecting important features with ensemble algorithms for predicting patients' blood pressure status significantly improves the performance of these models.
