دانلود کتاب پیشرفت‌ها در مراقبت‌های بهداشتی با استفاده از یادگیری ماشین، جلد ۲

In recent years, the convergence of machine learning (ML), artificial intelligence (AI), and healthcare has opened transformative avenues for diagnosis, treatment, monitoring, and personalized medicine. The global healthcare ecosystem is witnessing an era where data-driven insights, predictive analytics, and automation are becoming indispensable. This book, “Advances in Healthcare using Machine Learning”, is a curated collection of state-of-the-art research contributions that demonstrate how machine learning techniques are being harnessed to address complex challenges in modern healthcare. Each chapter in this volume showcases diverse and impactful applications of machine learning in the medical domain—from predictive models for disease detection to sophisticated image processing techniques, explainable AI, and integration of semantic web frameworks for clinical data management. The book begins with foundational studies such as cervical cancer and heart disease prediction using ML and ensemble learning models with interpretability enhancements. It then progresses through cutting-edge topics like biomedical signal denoising using traditional and deep learning techniques, retinal disease detection through deep segmentation networks, and novel architectures for liver segmentation from CT scans. Significant attention has also been devoted to areas such as digital pathology using self-supervised learning, personalized treatments enabled by Digital Twin technology, and the role of nature-inspired computing in creating intelligent and adaptive healthcare systems. Furthermore, innovative solutions like NLPdriven homeopathy recommendation systems and semantic web applications for enhancing clinical trial data interoperability highlight the growing scope of interdisciplinary approaches. This book is intended for researchers, practitioners, healthcare professionals, and students who aspire to understand and contribute to the next generation of intelligent healthcare systems. The chapters are contributed by international experts and reflect not only theoretical innovation but also practical relevance and real-world deployment potential. We sincerely hope that this compilation inspires further research and development in AI-driven healthcare solutions and contributes meaningfully to improving patient care, operational efficiency, and the global healthcare landscape.