Abstract
Heart disease remains one of the leading causes of mortality worldwide. Early detection and accurate diagnosis are crucial for effective treatment and prevention of cardiac complications. Artificial neural networks (ANNs) have emerged as powerful tools for heart disease detection, leveraging their ability to learn complex patterns from data. This chapter comprehensively reviews recent studies and developments in the application of ANNs for heart disease detection, highlighting their strengths, challenges, and future directions. The chapter also explores opportunities for the field, imagining the use of federated learning for collaborative model development, the integration of AI-driven decision support systems into standard clinical workflows, and the use of explainable AI techniques to improve model interpretability. It investigates a number of methods, such as the integration of multimodal data sources, convolutional neural networks (CNNs) for image-based diagnosis, risk prediction models, and ECG analysis.
Top1. Introduction
Heart failure (cardiovascular disease, coronary artery disease or heart disease), arrhythmias, and congenital heart anomalies are among the disorders that collectively constitute heart disease, which is a widespread health concern worldwide. In order to improve patient outcomes and lower death rates, prompt and correct diagnosis is essential. Since ANNs can model complex relationships in data, they present a promising way to improve efficiency and accuracy of heart disease detection. Cardiovascular disease (CVD) is one of the main causes of death (Ahsan et al., 2022). Poor dietary practices, inactivity, drunkenness, and tobacco use are a few risk factors that may accelerate heart-related problems. Consequently, the patients exhibit intermediate-risk factors (e.g.,such as high blood pressure and glucose, overweight and obesity). However, unanticipated and premature deaths can be avoided by early detection of high-risk individuals for CVD and provision of appropriate medications (Ahsan et al., 2022).
CVD (heart disease) is a phenomena that impact heart or veins and arteries (Alsalamah, 2017). Heart disease is now considered to be a second epidemic that causes of a lot of death in many nations. Early detection can reduce the death rate from heart disease. An imaging technique called echocardiography is used to diagnose heart issues. Echocardiography, also referred to as echo, is a non-invasive diagnostic tool that creates heart images using sound waves (Alsalamah, 2017).
Regression tasks may use MAE or RMSE to evaluate predictive performance (Zupan, 2003). Neural networks are one of the more popular data mining approaches. They have their roots in the field of AI, where they are sometimes shown as a computer's brain. Neural networks process data like human brain by including important characteristics of neurons found in the brain. A large portion of the nomenclature used in neural network theory and explanation comes from biology (Zupan, 2003).
It is possible to train data mining systems to recognize intricate correlations in data (Francis, 2001). Usually, data sets are big, with hundreds or even thousands of records and tens of thousands of independent variables. Their ability to fit nonlinearly related independent and dependent variables, and where precise shape of nonlinear relationship is unclear gives them an advantage over traditional statistical models used to analyze data, such as regression and ANOVA. In addition to offering benefits of their own, ANN, commonly known as neural networks, have same many benefits as other data mining methods. When the predictor variables are continuous as opposed to categorical, decision trees, which divide data into homogeneous clusters with comparable anticipated values for the dependent variable, perform better. When dealing with continuous and categorical information, neural networks perform admirably (Francis, 2001).
ANN are a class of relatively simple electrical models that are inspired by neural architecture of brain. Brain learns essentially by experience. It is an obvious example of how small, energy-efficient packages can handle some activities that are beyond the scope of current computers (Kohli et al., 2014). Brain modeling also promises a less technical method of developing machine solutions. This innovative computing technique provides a smoother reduction amid system overload when compared to its traditional counterparts. These computing techniques inspired by biology are thought to be the next big thing in computing industry (Kohli et al., 2014). Computers excel at routine jobs like ledger maintenance and complex algebra.
However, computers struggle to identify even basic patterns, much less translate previous patterns into future behaviors. Qualities of intelligence include reasoning, creativity, to name a few. Goal of this area of computer science is to emulate human behavior in computers (Kohli et al., 2014).
Key Terms in this Chapter
Echocardiography: The use of ultrasonography to check the heart is called echocardiography, or cardiac ultrasound. This kind of imaging is used in medicine and can be done using Doppler or conventional ultrasonography. An echocardiogram, sometimes known as a cardiac echo or just an echo, is the visual representation created by this method.
Electrocardiography: A recording of the electrical activity of the heart made by repeated cardiac cycles. This is a heart electrogram, which uses electrodes applied to the skin to create a graph of voltage vs time for the electrical activity of the heart. These electrodes pick up the minute electrical alterations brought about by the depolarization and repolarization of the heart muscle during each cardiac cycle (heartbeat).
Artificial Neural Networks: A technique in artificial intelligence that trains machines to handle data in a manner modeled after the human brain. Deep learning is a kind of machine learning technique that uses networked nodes or neurons arranged in a layered pattern to mimic the organization of the human brain.
Explainable AI: A collection of procedures and techniques known as explainable artificial intelligence (XAI) enables human users to understand and have faith in the output and outcomes produced by machine learning algorithms. The term “explainable AI” refers to an AI model's predicted effects and possible biases.
Angiography: An X-ray technique called angiography is used to examine blood arteries. Because blood vessels are not visible on a standard X-ray, your blood must first be infused with a special dye known as a contrast agent. This makes your blood vessels more visible, so your doctor can notice any issues.
Federated Learning: A central model is trained across decentralized devices or servers through the process of federated learning. The model is trained locally on each device, and only the model changes are communicated, as opposed to sending all data to a single location. By not disclosing raw data, this preserves privacy and permits collaborative learning.
Multimodal Data Sources: Clinical, omics, and imaging data are examples of data sources that must be ingested, categorized, and utilized to create analytics.
CNN: CNNs are a kind of neural network that are frequently utilized for computer vision and image recognition applications. Convolution is a technique used by CNNs to extract features from images. Sliding a tiny window across the image—also referred to as a filter or kernel—and applying a mathematical operation to its pixels constitute the convolution process. A feature map that highlights the salient characteristics of the image is the result of the convolution technique.
AI-Augmented Clinical Workflows: AI's incorporation into clinical trials represents a paradigm shift rather than merely an invention. It's about influencing the course of healthcare by improving patient and healthcare provider access to, personalization of, and transparency in clinical trials. We can speed the development of life-saving therapies, promote transparent and patient-centric trial environments, and transform clinical trial outcomes by utilizing AI. Artificial intelligence technology are integrated into numerous parts of healthcare delivery to improve patient outcomes, efficiency, and accuracy.
Risk Stratification: Through risk stratification, healthcare providers can determine which patient subgroups require what kind of care and services. It is the process of classifying patients as at-risk, using the results to guide treatment and enhance overall health outcomes.