Abstract
A visual analysis may accurately predict cardiac arrest, making it a potent educational tool for raising public awareness of health issues. By predicting cardiac arrest earlier, preventative steps can be taken to save lives, and the dissemination of such health knowledge can dramatically lower the world mortality rate. A heart attack, also known as cardiac arrest, encompasses various heart-related disorders and has been the leading cause of death worldwide in recent decades. Several medical data mining and machine learning technologies are being applied to gather helpful knowledge regarding heart disease prediction. The accuracy of the intended outcomes, however, is insufficient. This chapter aims to predict the likelihood of patients having a heart disease to solve the issue. Specifically, it compared alternative models for the identification of cardiac arrest to appropriately categorize and forecast heart attack instances with compact features. The use of ensemble algorithms over classifier algorithms gives a maximum accuracy of 96.5%, which is examined in this investigation.
TopIntroduction
The development of machine learning algorithms for predicting cardiac arrests is crucial for improving health education and outcomes for individuals at risk. Cardiac arrests can be sudden and unpredictable, making early detection and intervention essential for improving patient outcomes. By utilizing machine learning to predict cardiac arrests, healthcare providers and individuals can take proactive measures to address and prevent cardiac events, leading to better health outcomes and quality of life. Additionally, machine learning algorithms can provide more accurate and individualized predictions (Pilueta et al., 2022; Solanki et al., 2023; Uunona & Goosen, 2023), considering multiple factors such as age, lifestyle, and medical history. This enhances the effectiveness of health education and outreach programs, enabling individuals to make informed decisions about their health and take steps to reduce their risk of cardiac arrest. Overall, the development of machine learning algorithms for predicting cardiac arrests has the potential to revolutionize health education and lead to better health outcomes for individuals. By incorporating machine learning predictions into curricula (Almeida, 2023; Miranda & Tolentino, 2023; Tomé & Coelho, 2023), healthcare professionals can be trained to effectively use this tool to identify and respond to cardiac arrest incidents (Silva et al., 2023), thereby increasing the chances of saving lives.
Visual analysis is an effective technique for educating society. Raising public awareness of various health issues can lead to taking preventative actions (e.g., Arayata et al., 2022; Garcia et al., 2023; Parel et al., 2022). For instance, curative measures can be taken in advance to save human life (Maaliw, Alon, Lagman, Garcia, Susa, et al., 2022) through earlier cardiac arrest prediction. Promoting such health knowledge (Howard, 2023) in society can dramatically lower the world mortality rate. According to the World Health Organization (2021), cardiovascular diseases (CVDs) are the leading cause of mortality globally, killing an estimated 17.9 million people each year. Heart attacks and strokes are responsible for 85% of these fatalities. CVDs include coronary heart disease, cerebrovascular illness, rheumatic heart disease, and other diseases (Amini et al., 2021). Nearly three-quarters of all heart-related fatalities occur in low- and middle-income nations.
In 2015, low- and middle-income nations accounted for 82% of the 17 million early deaths (before the age of 70) related to non-communicable illnesses, accounting for a total of 37%. Four out of every five CVD fatalities are caused by heart attacks or strokes, among them, one-third of the deaths occur in those below 70-year old patients (Desai et al., 2021). High blood pressure, cholesterol, and lipid levels, as well as being overweight or obese, are all signs of heart illness (Jurgens et al., 2022). Identifying patients who are more likely to suffer from cardiac arrest and ensuring that they receive proper care would assist to prevent early deaths. All primary healthcare providers should give access to important noncommunicable disease drugs and basic health technologies to ensure that individuals in need receive treatment and counseling (Çalış et al., 2023; Lobo, 2023). Most heart attacks can be averted by addressing various behavioral risk factors such as cigarette use (Sandhu et al., 2012), poor nutrition and obesity (Garcia & Garcia, 2023), problematic alcohol consumption (Chudzińska et al., 2022), and lack of physical inactivity through population-wide initiatives.
Key Terms in this Chapter
Cardiac Arrest Prediction: Discover the factors that predict the likelihood of sudden cardiac arrest in people with structural heart disease.
Visual Awareness for Cardiac Arrest Prediction: It is a powerful learning tool for promoting health awareness in society by earlier predicting cardiac arrest. Visual analysis is a powerful instructional tool for educating the public about health issues since it may properly predict cardiac arrest. Preventative measures to save lives can be implemented via earlier cardiac arrest prediction.
Machine Learning comparative analysis: a comparative analysis is used for automated learning to analyze and evaluate several machine learning and deep learning models for the challenge of accurately forecasting cardiac arrest.
Health Educational Awareness for Cardiac Arrest Prediction: Excellent educational awareness is the foundation of a healthy lifestyle and one method for conducting health promotion and disease prevention initiatives that provide people with the chance to learn how to take care of themselves.
Disease Prediction Structure: The system model structure can estimate the likelihood of individuals developing heart illness using medical profiles such as age, sex, blood pressure, and blood sugar by using multiple machine learning and deep learning techniques and structures for the challenge of anticipating cardiac arrest.
Data Standardization: Data standardization is the process of developing standards and converting data from various sources into a uniform format that complies with the standards to enhance the model’s overall effectiveness in making predictions.
Supervised Learning Models: A machine learning paradigm known as supervised learning is used to solve issues where the available data consists of labeled instances, which means that each data point has features and a corresponding label.
Machine Learning Methods: Machine learning is an AI method that teaches computers to learn from experience. Instead of depending on a predetermined equation as a model, machine learning methods utilize computational intelligence techniques to learn and gain insights directly from data.