Gradient Boosting Based Model for Elderly Heart Failure, Aortic Stenosis, and Dementia Classification

Abstract

Cardiovascular diseases, specifically heart failure and aortic stenosis, are considered common and deadly, with the additional risk of developing dementia in the elderly population. Early diagnosis can help prevent or alleviate these diseases and potentially reduce mortality rates. Machine learning algorithms, especially gradient boosting (GB), can effectively predict the presence of these diseases through binary classification using demographic and medical data. However, research has yet to combine data from all three diseases for multiclass classification, which is the purpose of the present study. Using a dataset collected from Chiang Rai Prachanukroh Hospital, Chiang Rai, Thailand, a GB-based model is proposed for the multiclass classification of elderly people with heart failure, aortic stenosis, and dementia, with the inclusion of feature engineering techniques for maximum accuracy. Other existing methods, including decision tree, support vector machine, k-nearest neighbors, random forest, and extra trees were applied for comparison. The Optuna framework was used with the tree-structured Parzen estimator for hyperparameter optimization. The results produced by each classifier were compared using various performance metrics, namely precision, recall, F1 score, accuracy, the area under the receiver operating characteristic curve, the area under the precision-recall curve, and the Matthews correlation coefficient. The results are presented separately for each machine learning algorithm for comparison. Based on these metrics, it can be concluded that our proposed GB-based model outperformed other comparative models after applying feature engineering techniques.