A hybridization of XGBoost machine learning model by Optuna hyperparameter tuning suite for cardiovascular disease classification with significant effect of outliers and heterogeneous training datasets.
Journal:
International journal of cardiology
PMID:
39615697
Abstract
BACKGROUND: Over the last few decades: heart disease (HD) has emerged as one of the deadliest diseases in the world. Approximately more than 31 % of the population dies from HD each year. The Diagnosis of HD in an earlier stage is a cognitively challenging task due to the vast and complex availability of medical datasets. Many tests are available for the diagnosis of HD, such as ECG, etc.; but the proper diagnosis of the disease is still a great challenge.