"Machine Learning Predicts Thyroid Disorder from Spectroscopy" by M. T. Raghuraman is an innovative and comprehensive guide that explores the use of machine learning techniques for predicting thyroid disorders from spectroscopy data. With the increasing demand for early diagnosis and treatment of thyroid disorders, the book focuses on the potential of machine learning algorithms for medical diagnosis in the context of artificial intelligence and health care.
The book covers a wide range of topics, from pattern recognition and feature selection to supervised and unsupervised learning, as well as the use of decision trees, support vector machines, random forests, neural networks, and deep learning for predictive modeling. The author highlights the importance of data science and statistical learning in thyroid disorder prediction and early detection, and the role of biomarkers in precision medicine and endocrinology.
The book also delves into medical imaging, molecular diagnosis, bioinformatics, and multivariate analysis, and provides insights into the use of high-throughput technologies for feature extraction and model interpretation. The author discusses the impact of signal processing, spectral analysis, and dimensionality reduction on machine learning performance, and explores the role of computational biology in proteomics, metabolomics, genomics, and transcriptomics.
Overall, "Machine Learning Predicts Thyroid Disorder from Spectroscopy" is an essential resource for researchers, clinicians, and students in the fields of biomedical engineering, biostatistics, and medical diagnosis. The book offers a detailed and comprehensive analysis of the use of machine learning techniques for thyroid disorder prediction, and provides practical insights into the development and evaluation of predictive models for health care applications.
