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which are frequently used in classification problems, are also used in clustering and optimization processes. Although the simplest neural network model is perceptron, multilayer perceptron is often used in classification problems. Deep learning methods, which have been used in many important tasks recently, are based on ANNs. The adaptability and parallel processing capability of ANNs make them a powerful option for many problems.

      1.6.2.2 Unsupervised Learning

      Unsupervised learning works with untagged data and its purpose is to create clusters based on the characteristics of the data. Unlike supervised learning, untagged data is used instead of labeled data. After the data are divided into groups according to their similarity or distance, labeling is done with the help of an expert. Two applications that stand out in unsupervised learning are clustering and association rule mining. Clustering is the assignment of data points to groups called clusters. It has two types: partitioned and hierarchical methods. In partitioned clustering, a data point can only be in one cluster. In hierarchical clustering, a point can be hierarchically located in more than one cluster. In association rules mining, association rules focused on finding rules based on relationships between events are used in mining relationships between attributes.

      1.6.2.2.1 K-Means

      1.6.2.2.2 Apriori Algorithm

      1.6.3 Machine Learning Supported HF Studies

      Takcı [42] introduced a framework for the diagnosis of heart attack. In his study, in which the most successful classifier combination was sought with 12 different algorithms and four different feature selection methods, the most successful classifier was SVM using the linear kernel and the most successful feature selection method was the ReliefF algorithm. The obtained classification accuracy was reported as 84.81%.

      Non-invasive techniques, such as electrocardiography, or invasive techniques, such as blood tests, which are used to diagnose HF, also measure irregularities in values. Imbalances and anomalies are measured with artificial intelligence techniques, such as the process performed with existing diagnostic techniques. Previously used conventional diagnostic techniques work by increasing capacity with the support of artificial intelligence. For example, it will be possible to increase the accuracy of diagnosis thanks to electrocardiography supported by artificial intelligence.

Author Method Study
Guidi et al. [24] ANN, SVM, decision tree, fuzzy genetic algorithm Clinical decision support system for HF
Elfadil et al. [25] Neural nets and spectral analysis HF patients grouping
Gharehchopoghi et al. [26] ANNs Decision support system for HF
Candelieri et al. [27] Decision tree To determine patient stabilization

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