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Interval Type-3 Fuzzy Systems for Enhanced Heart Rate Classification in Reducing the Risk of Cardiovascular Disease
Ivette Miramontes, Oscar Castillo, Juan R. Castro and Patricia Melin
At present, the volume of data has acquired a fundamental role in various sectors. Increasing digitalization and advanced technological capabilities to store and process large sets of data have revolutionized decision making and strategy development by organizations. Additionally, the effective management of uncertainty in large-scale data processing has become an essential component to guarantee the precision and relevance of the extracted information, which is why Interval Type-3 Fuzzy Systems have recently been introduced, which help to work on a broader spectrum of information uncertainty. This study focuses on evaluating the performance of Interval Type-3 Fuzzy Systems in the medical field, particularly in the classification of Heart Rate, which is an important indicator to determine the risk of certain cardiovascular conditions in a person. To carry out the corresponding experimentation, a sample of thirty patients is used, considering variables such as age and heart rate. We experimented with different Membership Functions in the fuzzy classifiers to determine which of them offered the best results. In addition, a comparison is carried out with Type-1 and Interval Type-2 Fuzzy Systems. The results indicated that the Interval Type-3 Fuzzy System achieved a classification rate of 100%, exceeding the 96.6% efficiency obtained by the Type-1 and Interval Type-2 Fuzzy classifiers. The improvement in the classification accuracy of the interval Type-3 fuzzy system underlines the effectiveness of trapezoidal membership functions in medical applications.
Keywords: Interval Type-3 Fuzzy System, Heart Rate, Fuzzy Classification