The Second-Leading Killer in the USA Shouldn't Be This Deadly

Written by Bruce Ratner and Adam Bonislawski, ....

Catching cancer early remains the single best way to combat a disease that is the second-leading killer in both the US and worldwide. But the vast majority of resources in the fight against cancer are devoted to relatively ineffective late stage treatments. 'Early Detection' examines this important anomaly in an accessible and expertly researched survey.

In a co-authorship that brings together the passion and urgency of someone touched deeply by the experience of cancer with the knowledge of a skilled science writer, Ratner and Bonislawski narrate compelling case studies across a range of screening programs and different forms of cancer.

In this book, you'll discover:

• Importance of Early Detection: Understand the critical role early detection plays in combating cancer and why it's more effective than late-stage treatments.


• Expert Insights and Research: Gain knowledge from expertly researched information on various screening programs and the scientific principles behind them.


• Compelling Case Studies: See the personal and emotional side of cancer through stories that bring urgency and passion to the importance of early detection.


• Necessary Social Change: Understand the need for government and medical establishment support in expanding screening resources, particularly to economically disadvantaged and traditionally underserved communities.

Early detection remains our most powerful tool in the fight against cancer, offering hope and better futures for countless individuals.

Pick Up Your Own Copy to Learn How Together, We Can Save Lives with Early Cancer Detection!

The Second-Leading Killer in the USA Shouldn't Be This Deadly

Written by Bruce Ratner and Adam Bonislawski, ....

Catching cancer early remains the single best way to combat a disease that is the second-leading killer in both the US and worldwide. But the vast majority of resources in the fight against cancer are devoted to relatively ineffective late stage treatments. 'Early Detection' examines this important anomaly in an accessible and expertly researched survey.

In a co-authorship that brings together the passion and urgency of someone touched deeply by the experience of cancer with the knowledge of a skilled science writer, Ratner and Bonislawski narrate compelling case studies across a range of screening programs and different forms of cancer.

In this book, you'll discover:

• Importance of Early Detection: Understand the critical role early detection plays in combating cancer and why it's more effective than late-stage treatments.
• Expert Insights and Research: Gain knowledge from expertly researched information on various screening programs and the scientific principles behind them.
• Compelling Case Studies: See the personal and emotional side of cancer through stories that bring urgency and passion to the importance of early detection.
• Necessary Social Change: Understand the need for government and medical establishment support in expanding screening resources, particularly to economically disadvantaged and traditionally underserved communities.

Early detection remains our most powerful tool in the fight against cancer, offering hope and better futures for countless individuals.

Pick Up Your Own Copy to Learn How Together, We Can Save Lives with Early Cancer Detection!

Interest in predictive analytics of big data has grown exponentially in the four years since the publication of Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition. In the third edition of this bestseller, the author has completely revised, reorganized, and repositioned the original chapters and produced 13 new chapters of creative and useful machine-learning data mining techniques. In sum, the 43 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature.

What is new in the Third Edition:

• The current chapters have been completely rewritten.
• The core content has been extended with strategies and methods for problems drawn from the top predictive analytics conference and statistical modeling workshops.
• Adds thirteen new chapters including coverage of data science and its rise, market share estimation, share of wallet modeling without survey data, latent market segmentation, statistical regression modeling that deals with incomplete data, decile analysis assessment in terms of the predictive power of the data, and a user-friendly version of text mining, not requiring an advanced background in natural language processing (NLP).
• Includes SAS subroutines which can be easily converted to other languages.

As in the previous edition, this book offers detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. The author addresses each methodology and assigns its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.

Interest in predictive analytics of big data has grown exponentially in the four years since the publication of Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition. In the third edition of this bestseller, the author has completely revised, reorganized, and repositioned the original chapters and produced 13 new chapters of creative and useful machine-learning data mining techniques. In sum, the 43 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature.

What is new in the Third Edition:

• The current chapters have been completely rewritten.
• The core content has been extended with strategies and methods for problems drawn from the top predictive analytics conference and statistical modeling workshops.
• Adds thirteen new chapters including coverage of data science and its rise, market share estimation, share of wallet modeling without survey data, latent market segmentation, statistical regression modeling that deals with incomplete data, decile analysis assessment in terms of the predictive power of the data, and a user-friendly version of text mining, not requiring an advanced background in natural language processing (NLP).
• Includes SAS subroutines which can be easily converted to other languages.

As in the previous edition, this book offers detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. The author addresses each methodology and assigns its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.