Artificial Intelligence and Data Science in Healthcare Applications

This book provides an in-depth examination of how AI and data science are transforming predictive analytics and outlier detection in every industry. With in-depth examinations of machine learning, neural networks, NLP, and ethics of AI, it prepares readers with theoretical principles and practical tools to create smart, scalable systems.

Artificial Intelligence and Data Science in Healthcare Applications provides a thorough in-depth examination of how AI and data science are transforming predictive analytics and outlier detection in every industry. With in-depth examinations of machine learning, neural networks, NLP, and ethics of AI, this book prepares readers with both theoretical principles and practical tools to create smart, scalable systems. Real-world healthcare, cybersecurity, and finance case studies exemplify real world applications, and tutorials with leading libraries serve as a starting point for implementation. Features:Offers a broad overview of both foundational and advanced topics in artificial intelligence and data science, focusing particularly on their applications in prediction and detection. Addresses the ethical implications and social impact of artificial intelligence and data science, discussing topics such as algorithmic bias, privacy, and the ethical use of predictive technologies. Discusses enhanced deep learning model to detect lung diseases from Chest-Xray. Highlights lung cancer prediction using variational autoencoders and early stopping for neural network clustering and optimal tuning. Evaluates mental well-being through wearable sensors utilizing machine learning. It will serve as an ideal text for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communications engineering, computer engineering, information technology, and biomedical engineering.