Data-Driven Diagnostics and Disease Prediction with AI Optimization

Grab the future of clinical decision-making with Data-Driven Diagnostics and Disease Prediction with AI Optimization by Shailendra Pratap Singh. This authoritative guide bridges cutting-edge artificial intelligence and practical healthcare applications, showing how machine learning, deep learning, and optimization techniques can improve diagnostic accuracy and predictive modeling across modern health systems.

Inside, you’ll find clear explanations of core algorithms, step-by-step workflows for building predictive models from EHR and imaging data, and discussions of validation, interpretability, and regulatory considerations. Real-world examples and case studies illustrate applications in oncology, cardiology, radiology, and public health — making complex concepts accessible for clinicians, data scientists, biomedical engineers, and healthcare leaders.

Why this book matters: healthcare organizations worldwide are investing in AI to reduce diagnostic delays and personalize treatment. Whether you work in hospitals in North America, research labs in Europe, clinics across Asia, or health tech startups anywhere, this book gives actionable frameworks for deploying robust clinical AI — from data preprocessing and feature engineering to model evaluation and lifecycle management.

Practical, authoritative, and future-focused, this volume equips you to translate technical advances into safer, more effective patient care. If you’re seeking a reliable resource to master data-driven diagnostics, implement disease prediction pipelines, and navigate ethical and operational challenges of clinical AI, this is essential reading.

Bring AI optimization into your practice or research—order Data-Driven Diagnostics and Disease Prediction with AI Optimization today and start transforming data into better patient outcomes.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.