Bio-Inspired Optimization for Medical Data Mining

11Diabetes Prognosis Model Using Various Machine Learning Techniques

Pawan Kumar Patidar*, Manish Bhardwaj and Sumit Kumar

Department of Computer Science & Engineering Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur, India

Abstract

With the same speed that the 21st century asks of us, it is easy to believe that the fields of illnesses and their spread have expanded substantially in a world of quick paces and widespread adoption of bad health care practices. It is a collection of business intelligence (BI) tools that use cutting-edge machine learning techniques to uncover relationships and patterns in vast amounts of data. We can predict behaviors and events thanks to these data-driven linkages and patterns. Predictive analytics provides you a look into the future by using past events. It is important to note that the predictive model was built using previous predictive approaches, even if it is not based on the production of a mathematical model or algorithms for the development of the forecast. Instead, it uses algorithms that are built into the identified tool. It is suggested that, via the usage of the model, the organizations that provide both public and private health services adopt it in a commercial setting, using the model’s predictive capabilities for the client’s diagnosis and the optimization of consultation procedures.

Keywords: Diseases, diabetes, machine learning, prediction, supervised learning, unsupervised learning

11.1 Introduction ...