Predicting Cancer: Leveraging Machine Learning Techniques on Clinical Data Sets

Description

Predicting Cancer: Leveraging Machine Learning Techniques on Clinical Data Sets. this book contains multidisciplinary proficiency into actionable knowledge. The book:
Demystifies Complex Workflows: From raw EHR data preprocessing (Python/Pandas) to ensemble model deployment.
Prioritizes Clinical Relevance: Case studies on breast, lung, and oral cancers prevalent in South Asia.
Introduces Quantum Readiness: Quantum PCA and Q-SVM implementations for genomic datasets.
Offers Open-Source Tools: GitHub repositories with Jupyter notebooks for biomarker discovery.

About The Aurhor
Amit Awasthi is a seasoned technologist specializing at the convergence of healthcare analytics and advanced computing. With over 15 years of expertise in Python programming, machine learning (ML), and artificial intelligence (AI), having pioneered scalable frameworks for clinical data mining across leading research institutions in India. our work bridges theoretical algorithms and real-world medical applications, focusing on oncology informatics.
As a quantum computing researcher, I integrates quantum-enhanced ML to accelerate genomic pattern recognition—reducing computational bottlenecks in cancer risk modeling.