hyOPTXg: OPTUNA hyper-parameter optimization framework for predicting cardiovascular disease using XGBoost

Cardiovascular disease is a dangerous disorder that causes the most significant number of deaths across the world. In the past years, researchers proposed several automated systems to identify heart disease early so that there is a chance to enhance the diagnosis process. This research paper proposes an expert model called hyOPTXg, which predicts heart disease using an optimized XGBoost classifier. To produce a better system using a classifier, we need decent hyper-parameter tuning. So, we tuned the hyper-parameters of XGBoost and trained the model using tuned parameters. The framework used for hyper-parameter tuning is OPTUNA (hyperparameter optimization technique). This system was tested on three datasets: The Cleveland dataset and the Heart Failure prediction dataset from Kaggle and heart disease UCI from Kaggle. We used various metrics to assess the system's efficiency, including recall, precision, f1-score, accuracy, and the ROC chart's area under the curve (AUC). And we got better results compared to other systems which other authors propose in recent times. We got 94.7% in the Cleveland dataset,89.3% in the Kaggle heart failure dataset, and 88.5% in the heart disease UCI Kaggle dataset. Our prediction result says that we got better results than other systems proposed by the other authors. And we did a comparison study of our model with other authors' models.

Automated summary

This research proposes an expert model called hyOPTXg, which utilizes an optimized XGBoost classifier to predict cardiovascular disease. Through hyper-parameter tuning and training with the OPTUNA framework, the system achieves better results compared to other systems on various datasets.