Search

A recent study conducted at the University of Jeddah in Saudi Arabia focused on heart disease and the challenges of using machine learning algorithms with mixed data in clinical settings. The researchers proposed a hybrid model that combines unsupervised and supervised learning techniques to improve the accuracy and quality of heart disease diagnosis. The model consists of collaborative clustering and ensemble learning, which optimize different clustering algorithms and meta-classifier outcomes. The study concludes that this collaborative approach is effective in predicting heart disease. For more information, readers can refer to the journal article published in Applied Sciences. [Extracted from the article]