Название: Artificial Intelligence and Data Science in Healthcare Applications
Автор: Ashwani Kumar, Gautam Srivastava, P. K. Gupta, Mohit Kumar
Издательство: CRC Press
Серия: Computational and Intelligent Systems
Год: 2026
Страниц: 286
Язык: английский
Формат: pdf (true), epub
Размер: 26.1 MB

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.

Chapter 1 deals with the automated diagnosis of ADHD disease with a few state-of-the-art Artificial Intelligence algorithms. The experimental study and the results obtained after implementing advanced Deep Learning approaches to EEG signals specifically for the classification of ADHD. This research incorporates six models: Temporal Convolutional Network, Temporal Convolutional Network with Attention, Recurrent Neural Network, Graph Neural Network, CNN with GRU, and a hybrid model that has CNN, RNN, and Attention. Having a significant literature review of numerous relevant and recent research works along with the experimental details, this chapter can be a significant part of this book.

Jupyter Notebook is a highly valuable tool for enhancing and presenting interactive Data Science projects. It allows users to combine code and its results into a single file, which can include mathematical calculations, explanatory text, visualizations, and other useful content. Notebooks have gained significant popularity in modern data science, analysis, and research due to their intuitive workflow, which facilitates iterative and rapid development. The proposed model was developed and trained using various Python Machine Learning libraries such as Scikit-learn, TFLearn, and TensorFlow.

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.