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Computation intelligence (CI) paradigms, including artificial neural networks, fuzzy systems, evolutionary computing techniques, and intelligent agents, form the basis of making clinical decisions. This book explains different aspects of the current research on CI technologies applied in the field of medical diagnosis. It discusses critical issues related to medical diagnosis, like uncertainties in the medical domain, problems in the medical data, especially dealing with time-stamped data, and knowledge acquisition. This book is aimed at researchers, professionals, and graduate students in computational intelligence, signal processing, imaging, artificial intelligence, and data analytics.
Prediction of Diseases Using Machine Learning Techniques
A Novel Virtual Medicinal Care Model for Remote Treatments
Artificial Intelligence in Future Telepsychiatry and Psychotherapy for E-Mental Health Revolution
Optimized Convolutional Neural Network for Classification of Tumors from MR Brain Images
Predictive Modeling of Epidemic Diseases Based on Vector-Borne Diseases Using Artificial Intelligence Techniques
Hybrid Neural Network-Based Fuzzy Inference System Combined with Machine Learning to Detect and Segment Kidney Tumor
Classification of Breast Tumor from Histopathological Images with Transfer Learning
Performance of IoT-Enabled Devices in Remote Health Monitoring Applications
Applying Machine Learning Logistic Regression Model for Predicting Diabetes in Women
Compressive Sensing-Based Medical Imaging Techniques to Detect the Type of Pneumonia in Lungs
Electroencephalogram (EEG) Signal Denoising Using Optimized Wavelet Transform (WT): A Study
Predicting Diabetes in Women by Applying the Support Vector Machine (SVM) Model
Data Mining Approaches on EHR System: A Survey
Chest Tumor Identification in Mammograms by Selected Features Employing SVM
A Novel Optimum Clustering Method Using Variant of NOA
Role of Artificial Intelligence and Neural Network in the Health-Care Sector: An Important Guide for Health Prominence