Predicting Heart Failure. Группа авторов

understand language have been developed; with the help of image processing techniques, systems close to human vision have been developed; systems that imitate human learning, systems that extract information, systems that establish connections have been developed; and studies that reveal abnormal situations have been conducted.

      The main reason artificial intelligence-powered solutions have come to the fore is their ability to exploit many advantages of information technologies. Cheaper storage units, the increase in the capacities of the processing units, and advances in artificial intelligence algorithms make the field more popular every day. The cheaper storage units and the increase in their capacity have enabled more patient data to be stored. The increase in capacity and speed in processor units makes it possible to analyze data in a way not possible in the past. In fact, data collection and transfer possibilities from remote units have increased due to the developments in network technologies. All developments favor artificial intelligence and data science, and many jobs have become fulfilled with the help of artificial intelligence and data science.

      The developments have also contributed to artificial intelligence programming skills. With artificial intelligence, it has become possible to solve many problems that could not be solved with classical programming skills, and while complex data relationships cannot be solved with classical methods, recently relationships between data have become easily inferred.

      One of the areas most supported by artificial intelligence is computer-aided decision making, thanks to its various capabilities, notably diagnosis and prediction. Artificial intelligence-based diagnostic systems can be seen as an example of a non-invasive procedure, because there is no interference with the body in artificial intelligence supported clinical decision support systems, in which expert systems make decisions based on both expert opinions and machine learning systems’ modeling from past case examples. These systems, which are sometimes used separately, are used together in some places. With the increase in the studies on artificial intelligence, its subfields have emerged. There are many artificial intelligence subdomains, each with different characteristics. Among these subareas, machine learning, deep learning, expert systems, and image processing in particular provide auxiliary features in computer-aided decision making.

      1.5.1.1 Expert Systems

Expert systems are based on imitating human expertise with machines [17]. They are considered among the first successful applications of artificial intelligence. An expert system is often used in decision support systems development. In addition, the expert system has two components: the knowledge base and the inference engine.

      1.5.1.2 Machine Learning

Machine learning is the most prominent area among the artificial intelligence subareas. It is a field focused on developing systems that learn with the help of historical data. As a result of learning, they can classify new data, cluster, establish similarities between data, and capture other relationships. Although supervised and unsupervised learning algorithms are often prominent in machine learning algorithms, semi-supervised and reinforced learning methods are also supported. The main purpose of machine learning algorithms is to aid decision making [18].

      1.5.1.3 Deep Learning

      Deep learning is a sub-branch of machine learning. It is also recognized as the most powerful alternative to machine learning. It can perform more complex operations with fewer data. In addition, while feature selection is performed manually in traditional machine learning algorithms, this process is automatic in deep learning. The working principle of deep learning algorithms is the working principle of the brain. It is based on densely multilayered neural networks, but constrained Boltzman machines and probabilistic graph models are also associated with deep learning. These are the methods that work with large amounts of data and reach the final output by further improving the results in each layer. It is supervised, unsupervised, or semi-supervised in terms of the type of education. Its prominent algorithms are convolutional neural network (CNN) and recurrent neural network (RNN). Deep learning models have found use in many areas from natural language processing to image processing.

      1.5.1.4 Image Processing

Image processing is one of the most important application areas of artificial intelligence. With the help of image processing techniques, visual features are converted into numerical features and thus diagnostic studies are carried out. It refers to studies based on finding the patterns in the images. Especially with the help of 3D image processing, the borders of the diseased areas can be determined (Figure 1.1). Yang et al. [19] conducted a study of the 3D reconstruction of coronary vessels from angiography images, thus providing the possibility of better diagnosis of CAD. In the study, an aperture camera model and optimization methods were used.

Figure 1.1 Images for boundary detection in different views of heart, with permission from Springer Nature [20].

      1.5.2 Artificial Intelligence Supported HF Diagnostic Studies

With the use of artificial intelligence in health and especially in heart diseases, it has been possible to estimate the risk, diagnoses, and work flows and to apply sensitive treatment methods [21]. Thanks to artificial intelligence techniques, it has been possible to interpret large-sized data sets and thus obtain a more precise diagnosis. Within the artificial intelligence discipline, machine learning, image processing, robotics, and even natural language processing artificial intelligence techniques will be able to participate in the early detection and diagnosis of HF as well as outcome prediction and prognosis evaluation [22].

In a proposed study to help clinicians diagnose HF at an early stage, a scoring model based on a support vector machine (SVM) has been proposed. According to this model, samples were divided into three groups as healthy group, HF prone group, and HF group. The overall accuracy of the model in classification was 74.4% [23].

A study by Guidi et al. [24] was designed to help non-field experts make decisions in the analysis of HF. The system is based on three functional parts: diagnosis (severity assessment), prognosis, and follow-up. Four artificial intelligence techniques are used in the diagnostic function: artificial neural network (ANN), SVM, decision tree, and a fuzzy system with genetic algorithm support. A new technique for identifying HF patients using spectral analysis and neural networks was investigated in a study by Elfadil et al. [25]. A data set was used in the study, with 17 of the 53 samples being normal and the rest being patients. HF patients were divided into four groups with an accuracy of 83.65%.

Gharehchopogh et al. [26] presented a decision support model to help physicians in the study. The model, based on an ANN, was able to detect the presence or absence of HF with 85% accuracy. The study is within the scope of medical data mining.

Candelieri et al. developed a decision tree to determine patient stabilization [27] (with 88% accuracy). Pecchia et al. used decision tree techniques to classify patients into three severity groups using heart rate variability (HRV) measurements (HF: Healthy = 96% accuracy; Severe–Moderate = 79.3% accuracy) [28].

      1.6 Machine Learning Supported Diagnosis

      Invasive and non-invasive methods offer a wealth of diagnostic information. However, the interpretation of the available information can only be possible with the help of a physician. The increase in heart patients and the increase in patient data in parallel make it difficult to evaluate the data and extract information from them day by day. The intersection of the symptoms of heart disease with the symptoms of other diseases also makes the diagnosis of the disease a difficult problem. For this reason, there is a need to evaluate the data obtained with the help of invasive and non-invasive techniques with intelligent analysis tools in order to increase the diagnostic accuracy. Artificial intelligence