Data mining and machine learning methods are analyzed by [1] which were used to detect cardiac disease. Techniques for machine learning have been shown to be useful in a number of actual data mining techniques. Professional versions of these methods, as well as effective interfaces to multiple databases and also well user interfaces are now available from dozens of firms across the world. However, these first-generation methods have serious drawbacks. They usually presume that the data only has numerical and symbolic properties and that there is no text, visual features, or raw sensor readings. They presume the information has been meticulously gathered into a single database for a data mining purpose [22]. A model for predicting cardiac disease using data mining technologies such as the naive Bayes classifier and the decision tree. The validation tests are used to forecast the classifier's efficiency. For the provided dataset, the decision tree outperforms the naive Bayes classifier, according to the research. The research evaluated the artificial neural networks (ANNs), decision tree, RIPPER classifier, and support vector machine to predict and diagnose heart diseases (SVM). Mining techniques was shown to be the least accurate of the four classifiers for disease prediction after rigorous testing. Furthermore, modern methods are frequently fully automated, obviating the requirement for knowledgeable users' input at important stages of data generalization search. The most important thing is to identify hidden patterns using data mining tools. The J48 techniques that use the UCI dataset have worse accuracy than the LMT methods.
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Neural fuzzy system (DNFS) [25] approaches for evaluating and forecasting various cardiac conditions are used. The research into the therapy of cardiac disease was examined in this publication. The major goal of this study is to develop a smart and low-cost system, as well as to improve the present system's efficiency. In this article, data mining methods are largely employed to enhance heart disease predictions. The SVM and neural networks show exceptionally promising outcomes in heart disease prediction, according to the findings of this study. Even data mining technologies are not promising when it comes to forecasting heart disease. A fuzzy decision analysis system based on an optimization technique for forecasting heart disease risk levels. The fuzzy decision support system (FDSS) that we have presented works like this: (i) collect and process the dataset, (ii) choose effective characteristics using various approaches, (iii) use GA to construct weighed fuzzy rules tied to specific attributes, (iv) construct the FDSS using obtained fuzzy knowledge base, and (v) prediction heart disease tests using real-world data reveal that the proposed creative technique is successful. Fuzzy logic is a subjective computational framework that is used to describe things in the real world that are inaccurate. The fuzzy logic system (FLS) is a decision making tool that is modelled after the fuzzy rule-based system (FRBS). The usage of DSSs in medical technology is on the rise these days. The decision support systems are used by the majority of systems which require a computer-aided system. Even though some institutions utilize DSSs, they are only capable of handling simple queries instead of more complicated ones. Clinical judgments are frequently made based on an expert's expertise and intuition, rather than the system's underlying patterns. As a result, the quality of service provided to patients is impacted. FRBS provides inaccurate data inputs. Fuzzy logic is not have systematic approach for solving problems. 2ff7e9595c
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