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Analysis of Heart Disease Prediction Using Machine Learning Techniques
Published in Saravanan Krishnan, Ramesh Kesavan, B. Surendiran, G. S. Mahalakshmi, Handbook of Artificial Intelligence in Biomedical Engineering, 2021
N. Hema Priya, N. Gopikarani, S. Shymala Gowri
Heart diseases or cardiovascular diseases are a class of diseases that involve the heart and blood vessels. Cardiovascular disease includes coronary artery diseases (CADs) like angina and myocardial infarction (commonly known as a heart attack). There is another heart disease called coronary heart disease, in which a waxy substance called plaque develops inside the coronary arteries that is primarily responsible for supplying blood to the heart muscle that is rich in oxygen. When plaque accumulates up in these arteries, the condition is termed as atherosclerosis. The development of plaque happens over many years. Over time, this plaque deposits harden or rupture (break open) that eventually narrows the coronary arteries, which in turn reduces the flow of oxygen-rich blood to the heart. Because of these ruptures, blood clots form on its surface. The size of the blood clot also makes the situation severe. The larger blood clot leads to flow blockage through the coronary artery. When time passes by, the ruptured plaque gets hardened and would eventually result in the narrowing of the coronary arteries. If the blood flow has stopped and is not restored very quickly, that portion of the heart muscles begins to die.
Medical Image Classification Algorithm Based on Weight Initialization-Sliding Window Fusion Convolutional Neural Network
Published in Varun Bajaj, G.R. Sinha, Computer-aided Design and Diagnosis Methods for Biomedical Applications, 2021
Ankit Kumar, Pankaj Dadheech, S. R. Dogiwal, Sandeep Kumar, Rajani Kumari
There is an amount of work to manipulate the output of the heart, many of which are tapering or obstruction of the coronary arteries because of coronary artery disease, usually caused by atherosclerosis. Atherosclerosis (occasionally vociferate the “hardening” or else “pressing” of the arteries) produces cholesterol along with pinged deposits (called plaques) which lies along the internal ramparts of the arteries. This plaque may confine the bloodstream to the heart strength via closing the artery or with irregular arterial tone and role. With sufficient blood delivery, the heart is filled with oxygen along with the essential nutrients that are required for it to function correctly. This can be the reason for the chest pain called angina. If the blood delivers to a part of the heart that is completely blocked, or if the energy needed by the heart exceeds its blood supply, a heart attack can occur.
ANFIS-Based Cardiac Arrhythmia Classification
Published in Archana Mire, Vinayak Elangovan, Shailaja Patil, Advances in Deep Learning for Medical Image Analysis, 2022
Alka Barhatte, Manisha Dale, Rajesh Ghongade
The electrocardiogram (ECG) is a cardiac signal representing the recording of the electrical activity of the heart. Information such as heart rate, rhythm, and morphology in the form of conduction disturbances can be extracted from the ECG signal. The significance of the ECG is notable in that coronary heart diseases are major causes of mortality worldwide. The ECG varies between different individuals, due to the anatomy of the heart, and differences in size, position, age, etc. Thus, the ECG yields highly distinctive characteristics, suitable for various applications and diagnosis. This chapter focuses on cardiac arrhythmia classification. Cardiac arrhythmia is a heart disorder displaying an irregular heartbeat due to malfunction in the cells of the heart’s electrical system. During cardiac arrhythmia, the heartbeat can have an irregular rhythm. Sometimes it is too fast – >90 beats/min – and this is called tachycardia; when the heartbeat is too slow – <60 beats/min – this is called bradycardia. Thus, there are many types of cardiac arrhythmia based on heart rate and site of origin. Some are frequently benign, although several may be a sign of significant heart disease, stroke, or surprising heart failure. At some stage in cardiac arrhythmia, the heart may not be capable of pumping enough blood to the body. Lack of blood flow can damage organs like the brain and heart. Thus, to enable appropriate survival measures, an accurate classification is required of cardiac arrhythmia that leads to heart rate variations. This chapter introduces the classification of six types of cardiac arrhythmias based on the adaptive neuro-fuzzy inference system (ANFIS).
Data Analytics for Risk of Hospitalization of Cardiac Patients
Published in IETE Journal of Research, 2023
M. Chandralekha, N. Shenbagavadivu
The onset of chronic disease especially in patients with heart disease is mostly influenced by the lifestyle and predicting heart disease early could save cost and life. Heart disease refers to problems that are associated with blood vessels, circulatory system, disease related to tissues and structural units. The symptoms of heart disease vary for men and women. Men experience chest pain while women experience shortness of breath, chest discomfort. However, the common symptom includes chest pain, breathlessness and palpitations. Other symptoms include that pain that migrates from chest towards arm, back, jaw, heavy sweating, nausea, dizziness. Abnormal heartbeats or arrhythmia have symptoms like fluttering feeling in the chest, high heart beat, low heart beat, dizziness, shortness of breath and dizziness. There are number of factors that can cause heart disease such as genetic factors, heart muscle damages, heart valve disorders, pumping conditions and life style. The main risk factors for developing heart disease are age, sex, family history, smoking, diet that have high salt, fat, sugar, high blood pressure, diabetes, high cholesterol levels, stress and anxiety.
Identifying heart disease risk factors from electronic health records using an ensemble of deep learning method
Published in IISE Transactions on Healthcare Systems Engineering, 2023
Linkai Luo, Yue Wang, Daniel Y. Mo
Heart disease is one of the leading causes of death worldwide. In the United States, heart disease and related diseases account for more than 600,000 deaths annually (CDC, 2022). The annual total cost due to heart diseases has been reported to reach 108.9 billion dollars, including medications, medical services, and lost productivity (Heidenreich et al., 2011). The development of heart disease is complicated and depends on numerous risk factors. The World Health Organization (WHO) defines these as “any attribute, characteristic or exposure of an individual that increases the likelihood of developing a disease or injury” (WHO, 2023). Medical research has indicated that risk factors related to heart disease include lifestyle factors such as smoking, hereditary factors such as family history of heart disease, and specific clinical conditions such as coronary artery disease (CAD), diabetes, obesity, hyperlipidemia, and hypertension (Dokken, 2008). Identifying and reducing potential risk factors are critically important for early prevention and treatment and to reduce the incidence of heart disease worldwide.
Exploring the use of association rules in random forest for predicting heart disease
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2023
Khalidou Abdoulaye Barry, Youness Manzali, Rachid Flouchi, Youssef Balouki, Khadija Chelhi, Mohamed Elfar
Heart disease is a disease that affects the heart and damages it over time. It is gaining ground day by day and killing all over the world. This is why researchers are developing models using machine learning algorithms to predict diseases to assist healthcare workers. In this research work, we have proposed a novel method that consists in improving the performance of the random forest technique by adding association rules to its training procedure. This novel method and five other classifiers namely Random Forest, Decision Tree, Support Vector Machine, Naïve Bayes, and XGBoost are programmed and tested in the same dataset which is the heart dataset Cleveland. Then, the comparison was made between them based on parameters such as model accuracy, model sensitivity, model specificity, model AUC, and log loss. The results showed that the novel method measured with these metrics performs better than the other five classifiers. It reaches 83.50% accuracy, specificity with 80.70%, sensitivity with 89.10%, 90.50% AUC, and 0.405 log loss. In addition, the classical random forest technique took the second position after the proposed model with 82.80% accuracy, specificity with 80.50%, sensitivity with 87.80%, 89.50% AUC, and 0.421 log loss.