Artificial Intelligence Applications for Health Care

This book provides an introduction to computational intelligence techniques for healthcare systems, covering topics such as fuzzy sets, artificial neural networks, deep neural networks, and genetic algorithms. It also discusses signal and image filtering techniques and includes studies with medical signal and image datasets.

Format: Hardback
Length: 312 pages
Publication date: 15 April 2022
Publisher: Taylor & Francis Ltd

Computational Intelligence (CI) techniques play a vital role in healthcare systems, offering innovative solutions for various challenges. Fuzzy sets, artificial neural networks (ANNs), deep neural networks (DNNs), and genetic algorithms (GAs) are among the prominent CI methods employed in healthcare. This comprehensive guide aims to provide a clear understanding of these techniques, enabling healthcare professionals to leverage their power for improved patient outcomes.

Fuzzy sets, a fundamental CI concept, are characterized by their imprecise and uncertain nature. They are useful in dealing with situations where data is incomplete or ambiguous. Fuzzy sets allow for the representation of vague concepts and the creation of rules that capture the inherent gray areas. By incorporating fuzzy logic, ANNs can learn from data and make predictions with a degree of uncertainty. This flexibility makes ANNs well-suited for tasks such as pattern recognition, classification, and decision-making.

DNNs, a subset of ANNs, are designed to mimic the workings of the human brain. They consist of multiple layers of interconnected neurons that process and analyze data. DNNs have shown remarkable capabilities in tasks such as image recognition, speech recognition, and natural language processing. By training DNNs on large datasets, they can learn complex patterns and make accurate predictions.

GAs, on the other hand, are optimization algorithms that search for the best solution among a set of possible options. They are widely used in healthcare for tasks such as drug discovery, patient diagnosis, and treatment planning. GAs simulate the process of natural selection, where individuals with favorable traits are selected to reproduce, leading to the evolution of a solution that maximizes a specific objective.

In addition to these techniques, CI in healthcare also involves signal and image filtering techniques. These methods are used to preprocess data, removing noise and enhancing features that are relevant to healthcare applications. Signal filtering techniques, such as Fourier transform and wavelet analysis, are used to analyze and interpret medical signals such as electrocardiograms (ECGs), electroencephalograms (EEGs), and electromyograms (EMGs). Image filtering techniques, such as morphological operations and thresholding, are used to process medical images such as X-rays, magnetic resonance imaging (MRI), and computed tomography (CT) scans.

Filting and classification problems are fundamental in healthcare CI. Filtering involves removing unwanted noise or artifacts from data, while classification involves categorizing data into different groups based on specific criteria. These problems are often encountered in medical imaging, where images must be analyzed to identify abnormalities or diagnose diseases.

Numerous studies have been conducted using medical signal (ECG, EEG, EMG) and image (X-rays, FMRI, CT) datasets to evaluate the performance of CI techniques in healthcare. These studies have focused on various aspects of CI, including accuracy, precision, recall, and signal-to-noise ratio (SNR). Accuracy measures the percentage of correct predictions made by a model, while precision measures the percentage of true positives among the predicted positives. Recall measures the percentage of true positives that are correctly identified, while SNR measures the strength of the signal relative to the noise.

In conclusion, computational intelligence techniques such as fuzzy sets, artificial neural networks, deep neural networks, and genetic algorithms play a crucial role in healthcare systems. These techniques offer innovative solutions for various challenges, including pattern recognition, classification, decision-making, drug discovery, and patient diagnosis. By providing a clear understanding of these techniques, healthcare professionals can leverage their power to improve patient outcomes and advance the field of healthcare.

Weight: 771g
Dimension: 234 x 156 (mm)
ISBN-13: 9781032148465