Optimized Predictive Models in Health Care Using Machine Learning - Tapa dura

Acerca del autor

Sandeep Kumar, PhD, is a professor in the Department of Computer Science and Engineering, K L Deemed to be University, Vijayawada, Andhra Pradesh, India. He has been granted six patents and successfully filed another ten. He has published more than 100 research papers in various national and international journals and proceedings of reputed national and international conferences.

Anuj Sharma, PhD, is a professor at Maharshi Dayanand University, Rohtak, India. He has 19 years of teaching and administrative experience and has published more than 50 journal articles.

Navneet Kaur, PhD, is a professor in the Department of Computer Science & Engineering, Chandigarh University, India. She is the awardee of the Best Engineering College Teacher Award for Punjab State for the year 2019 and has published more than 35 research articles in reputed SCI journals and conferences.

Lokesh Pawar, PhD, is an assistant professor at Chandigarh University, India. He has filed two patents and has published multiple research articles in many SCI journals.

Rohit Bajaj, PhD, is an associate professor in the Department of Computer Science & Engineering, Chandigarh University, India. He has 12 years of teaching research experience and has published 60 papers in refereed journals and conferences.

De la contraportada

This book is a comprehensive guide to developing and implementing optimized predictive models in healthcare using machine learning and is a required resource for researchers, healthcare professionals, and students who wish to know more about real-time applications.

The book focuses on how humans and computers interact to ever-increasing levels of complexity and simplicity and provides content on the theory of optimized predictive model design, evaluation, and user diversity. Predictive modeling, a field of machine learning, has emerged as a powerful tool in healthcare for identifying high-risk patients, predicting disease progression, and optimizing treatment plans. By leveraging data from various sources, predictive models can help healthcare providers make informed decisions, resulting in better patient outcomes and reduced costs.

Other essential features of the book include:

• provides detailed guidance on data collection and preprocessing, emphasizing the importance of collecting accurate and reliable data;
• explains how to transform raw data into meaningful features that can be used to improve the accuracy of predictive models;
• gives a detailed overview of machine learning algorithms for predictive modeling in healthcare, discussing the pros and cons of different algorithms and how to choose the best one for a specific application;
• emphasizes validating and evaluating predictive models;
• provides a comprehensive overview of validation and evaluation techniques and how to evaluate the performance of predictive models using a range of metrics;
• discusses the challenges and limitations of predictive modeling in healthcare;
• highlights the ethical and legal considerations that must be considered when developing predictive models and the potential biases that can arise in those models.

Audience

The book will be read by a wide range of professionals who are involved in healthcare, data science, and machine learning.