Precision Heart and Artery Disease Prediction Via Fusion of Machine Learning Algorithms and Turf-L1 Regularizations Technique
Keywords:
Cardio Vascular disease, Machine Learning Algorithm, L1 Regularization, TuRF, Feature SelectionAbstract
Coronary artery disease, which claims many lives each year, is the largest causes of human death. Due to our unhealthy lives, we have recently witnessed an exponential growth in several chronic diseases. The most prevalent and potentially fatal illness is cardiovascular disease, which raises the death rate dramatically. To preserve lives, it is essential to accurately diagnose cardiac illness at an early stage. Many existing cardiovascular disease detection algorithms face challenges such as redundant features, the curse of dimensionality, imbalanced datasets, and noise. As a result, their performance and efficiency are often compromised. The abundance of comprehensive medical diagnostic data has paved the way for the development of sophisticated machine learning and deep learning models, allowing for automated early detection of cardiac issues. Traditional methods, however, face limitations as they struggle to generalize effectively to novel data not encountered during training. The risk profile of the patients is evaluated using a variety of clinical criteria, which aids in an early diagnosis. The Cleveland, Beach, Switzerland, Hungary, and Stat datasets were combined. Appropriate features were selected using the TuRF (Tuned ReliefF)-L1 Regularizations technique. In the training phase, innovative fusion classifiers such as the Decision Tree Carrying Method (DTCM), Random Forest Carrying Method (RFCM), K-Nearest Neighbours Carrying Method (KNNCM), AdaBoost Pushing Method (ABPM), and Gradient Boosting Pushing Method (GBPM) were developed. These classifiers involve the integration of traditional classifiers with bagging and boosting methods. The most exact subsets of data are produced by the feature selection approach, which can be used to reliably forecast cardiovascular disease. And unequivocally show that the suggested CNN-Cardio Assistant system outperforms the current cutting-edge techniques. It is used a variety of performance criteria, including accuracy, precision, recall, and the F1 measure, to evaluate the effectiveness of the suggested approach. This method had a validation accuracy of 92.3%. The outcomes of the experiments show how effective the suggested strategy is in a practical setting.
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