Optimizing Prediction of Cardiac Conditions Using Hyper-Adaboost-Integrated Machine Learning Models

Recent advancements in machine learning have played a crucial role in the healthcare industry, particularly in predicting heart disease with assorted datasets. Despite the registration results indicating promising accuracy and resilience in heart disease prediction models, it is still low compared to what we expected. In this article, a new technique has been demonstrated to predict heart disease based on state-of-the-art machine learning with a focus mostly on Hyper Adaboost classifiers. We evaluate the performance of several key machine learning algorithms, including Random Forest, Extra Trees, LightGBM, Decision Tree, and Hyper Adaboost, on well-known heart disease datasets. Our results are mixed, but they suggest that the Hyper Adaboost classifier provides stronger accuracy, with performance metrics consistently exceeding 97%, much better than similar models. This research highlights the promise of newer packages under Hyper for detecting and screening individuals with heart disease early, which can offer insights into advancing methodologies in future studies as well as clinical use. Our technique exhibited strong predictive power, encouraging additional study with a larger and more comprehensive dataset for the validation of our model.