Ensemble Learning for Detection and Analysis of Depression in Women Using Machine Learning Approaches

One of the biggest global health issues is cardiovascular disease, which kills a staggering number of people every year. The requirement for early detection pushed the introduction of strong deep learning computational methods, which have enhanced diagnostic accuracy. The goal of this study is to provide a method for detecting cardiovascular disease risk factors using machine learning. This method combines Bagging the classification algorithms (BaC) and boosting basic learners (BoC), using the heart diseases databases given by UCI, to increase prediction accuracy. This kind of classifier takes into account important metrics including maximal heart rate, age, cholesterol levels, and resting pressure in the blood. We beforehand the dataset and run the model via a tenfold cross-validations procedure to ensure its correctness. In order to reach the highest possible level of efficiency, the Bagging Classifier employs a total of 200 estimators, whilst the Boosting Classifier makes use of 100 estimates and a learning rate of 0.134 throughout the training process. In terms of performance criteria such as data delivery rate and data packets loss rate, the suggested system is superior to the current system in terms of its overall performance. When the bagging and boosting classifiers are combined, the total result is superior than the results obtained by either approach alone 98% in data delivery rate. This system's exceptional success may be attributed to the increased reliability it has as a diagnostic instrument for facilitating the early identification of heart illness. It is possible to improve the generalizability of the model by using deeper learning models in a more efficient manner and by using bigger datasets.