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Technological tools and computational techniques have enhanced the
healthcare industry. These advancements have led to significant
progress in the diagnosis of heart disorders. Electrocardiogram
Signal Classification and Machine Learning: Emerging Research and
Opportunities is a critical scholarly resource that examines the
importance of automatic normalization and classification of
electrocardiogram (ECG) signals of heart disorders. Featuring a
wide range of topics such as common heart disorders, particle swarm
optimization, and benchmarks functions, this publication is geared
toward medical professionals, researchers, professionals, and
students seeking current and relevant research on the
categorization of ECG signals.
Advanced conceptual modeling techniques serve as a powerful tool
for those in the medical field by increasing the accuracy and
efficiency of the diagnostic process. The application of artificial
intelligence assists medical professionals to analyze and
comprehend a broad range of medical data, thus eliminating the
potential for human error. Medical Diagnosis Using Artificial
Neural Networks introduces effective parameters for improving the
performance and application of machine learning and pattern
recognition techniques to facilitate medical processes. This book
is an essential reference work for academicians, professionals,
researchers, and students interested in the relationship between
artificial intelligence and medical science through the use of
informatics to improve the quality of medical care.
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