Abstract
Heart disease is one of the leading causes of death both the United States and around the world, responsible for the deaths of some 17 million people annually. Many of these deaths could be either prevented or treated more effectively by early detection and subsequent behavioral changes. A model which can accurately predict which patients are at high risk of heart failure would therefore prove valuable for use by healthcare professionals. This paper proposes an approach using a collection of fuzzy inference systems (FIS), arranged in a fuzzy tree to identify which patients are at the highest risk of heart failure. The structure of the fuzzy tree is assumed a priori, then the membership functions and rule base (RB) for each individual FIS in the tree are tuned using a genetic algorithm (GA). The results of the approach are quantified using accuracy, F1 score, true positive rate, and true negative rate, then compared to the results obtained by a variety of different algorithms on the same dataset.
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DeGroote, N., Cohen, K. (2022). A Genetic Fuzzy Approach for the Prediction of Heart Failure. In: Rayz, J., Raskin, V., Dick, S., Kreinovich, V. (eds) Explainable AI and Other Applications of Fuzzy Techniques. NAFIPS 2021. Lecture Notes in Networks and Systems, vol 258. Springer, Cham. https://doi.org/10.1007/978-3-030-82099-2_9
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DOI: https://doi.org/10.1007/978-3-030-82099-2_9
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