PREDICTING HEART FAILURE INVASIVE, NON-INVASIVE, MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE BASED METHODS

• Other Authors: Sadasivuni, Kishor Kumar (Editor), Ouakad, Hassen M. (Editor), Al-Maadeed, Somaya (Editor), Yalcin, Huseyin C. (Editor), Bahadur, Issam Bait (Editor)
• Format: Book
• Language: English
• Published: Hoboken, NJ Wiley 2022
• Subjects: Positive > Pressure Respiration > methods; Ventilator Weaning > methods; Ventilators, Mechanical; Respiratory Insufficiency > physiopathology
• Online Access: Click Here to View Status and Holdings.

 Heart diseases are the deadliest disease in the world. There are many diseases in the heart disease category, the most prominent of them is coronary artery disease (CAD) that causes heart attack. CAD, high blood pressure and many other heart diseases cause HF. HF is a consequence of heart disease and the prediction of HF is related to the prediction of diseases in the category of heart disease. In this chapter, the diagnosis of HF is discussed as invasive / non-invasive and artificial intelligence / machine learning techniques. Invasive and non-invasive techniques make a distinction regarding the way the patient is treated. Invasive methods are usually associated with a physical intervention in the body. This intervention involves operations such as taking blood for blood analysis, not pressing strongly on the abdominal area. Non-invasive methods include methods such as physical therapy, blood pressure and temperature measurement. In today's world where information technologies have evolved in every field, the field of health has also received its share. Computer aided clinical decision support systems provide the strongest support to diagnostic studies today. The most important components of computer-aided diagnosis are artificial intelligence and machine learning. Artificial intelligence and machine learning systems offer a wide range of services from the smart assistant application to the use of imaging techniques. Artificial intelligence and machine learning have a healing role in electrocardiography, echocardiography and similar invasive and non-invasive techniques. In the second part of the chapter, HF will be described and its causes, symptoms and treatment will be revealed. The purpose of the third part is to explain the diagnosis by invasive and non-invasive methods. In the fourth chapter, computer aided diagnosis and decision support systems are briefly mentioned. In the fifth chapter, first what artificial intelligence is, then its fields and examples of artificial intelligence supported studies are presented. In the sixth chapter, what machine learning is, learning types, machine learning algorithms and machine learning based diagnostic studies are explained. Studies are summarized at the end of the chapter and diagnostic studies for HF are commented