Abstract
Heart failure is a severe medical case, where the heart is not able to function properly to maintain blood flow. The surgical heart transplant procedure accomplished with the last stage of failure of the heart. Machine learning approaches account an ability to handle large datasets systematically and are extensively used in a biomedical research field. With the help of machine learning algorithms, tools are developed that helps specialist as a successful mechanism. The objective of this study is to learn different machine learning approaches for analyzing the heart transplantation dataset by using suitable classification algorithm. Also, the theoretical and the experimental comparative study of different machine learning techniques, using heart transplantation data. This study provides basic guidelines on machine learning technique. The results provide an overview of machine learning technique. We have used a WEKA machine learning software for evaluation and analysis to get an easy way to understand the result.
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Kant, S., Jagtap, V. (2019). Comparative Study of Machine Learning Approaches for Heart Transplantation. In: Saini, H., Sayal, R., Govardhan, A., Buyya, R. (eds) Innovations in Computer Science and Engineering. Lecture Notes in Networks and Systems, vol 74. Springer, Singapore. https://doi.org/10.1007/978-981-13-7082-3_47
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DOI: https://doi.org/10.1007/978-981-13-7082-3_47
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