to download project abstract of ensemble machine learning

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In this paper, We present a system for identifying cardiac disease that is both efficient and
accurate that is based on machine learning techniques in this study.

The system employs a range of classification techniques such as logistic regression, K-nearest neighbor, Naive Bayes, and decision trees, complemented by standard feature selection algorithms. These strategies not only enhance classification accuracy but also optimize the system’s execution time by focusing on relevant features. To refine the model and ensure optimal performance, the leave-one-subject-out cross-validation method was utilized, allowing for robust model assessment and hyperparameter tuning.

Evaluation of the classifier’s performance involved the use of carefully selected features obtained through feature selection methods. Particularly, the proposed Feature Construction with Mutual Information Maximization (FCMIM) technique demonstrated promise when paired with a support vector machine classifier. Experimental results showcased the efficacy of this combined approach in constructing a sophisticated intelligent system tailored for identifying heart ailments.

Moreover, the proposed methodology exhibits practicality in healthcare applications, specifically in the early detection of cardiac issues. Its potential impact lies in its adaptability and ease of implementation within the healthcare industry. By leveraging FCMIM in conjunction with support vector machines, this approach presents a viable solution for developing robust diagnostic tools.

The system’s ability to harness varied classification techniques and feature selection methodologies, along with the successful integration of FCMIM with support vector machines, underscores its potential as a high-level intelligent system for cardiac issue identification. Its suitability for practical implementation within healthcare highlights its value as an efficient and reliable tool for early detection and diagnosis, potentially contributing significantly to improving patient outcomes in cardiovascular health.

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