Analytical Biomechanics

**33**

**Chapter 3**

**Abstract**

Applications

An Introduction to Survival

*Sheik Abdullah Abbas, Selvakumar Subramanian,* 

In today's world, data analytics has become the integral part of every domain such as IOT, security, healthcare, parallel systems, and so on. The importance of data analytics lies at the neck of what type of analytics to be applied for which integral part of the data. Depending upon the nature and type of data, the utilization of the analytical types may also vary. The most important type of analytics which has been predominantly used up in health-care sector is survival analytics. The term survival analytics has originated from a medical domain of context which in turn determines and estimates the survival rate of patients. Among all the types of data analytics, survival analytics is the one which entirely depends upon the time and occurrence of the event. This chapter deals with the need for survival data analytics with an explanatory part concerning the tools and techniques that focus toward survival analytics. Also the impact of survival analytics with the real world problem

**Keywords:** classification, data analytics, statistics, survival analytics, prediction,

Survival analysis refers to a branch of statistical analysis domain that evaluates the effect of predictors on *time until an event*, rather than the *probability of an event*, occurs. It is used to analyze data in which the time until the event is of interest. As the name indicates, this method has origins in the field of medical research for evaluating the impact of medicines or medical treatment on time until death. Survival analysis is also known as reliability analysis in the engineering discipline, duration analysis in the economics discipline, and event history analysis in the

The term is originated from a medical context in which it has been used to estimate the survival rate of patients. Data classification can be dealt explicitly with the process and paradigms available in survival analytical models [1]. The process

of survival analytics can be explored through various techniques such as:

Analytics, Types, and Its

*Parkavi Ravi, Suganya Ramamoorthy* 

*and Venkatesh Munikrishnan*

has been depicted as a case study.

**1. Introduction to survival analytics**

parametric models

sociology discipline.

### **Chapter 3**
