Using Data Mining Techniques to Predict the Survival Rate For Heart Transplants

This week , I just wanted to share something from my current research with my classmates here in my blog.

As you may have already known, the usage of data mining applications have been increasing tremendously in Healthcare and Bioinformatics.
My current research focuses on  Heart Transplantation Success which is a very critical problem for a long time. There is a big need for the donors in order to be able to meet the current demand. 

The demand for organ transplantation is increasing, while the number of donors remains the same, resulting in longer lists of patients waiting for transplantation

Did you know that, about 5.8 million people have heart failure in United States, with an annual estimated incidence rate of over 500,000.?

Approximately 5% of these patients have end-stage heart failure, a condition characterized by the urgent need to undergo a heart transplant. It is important to note that only a small fraction of these patients can undergo a heart transplant due to the scarcity of donated healthy hearts. 

About 3,000 people in the United States are on the waiting list for heart transplants on any given day while there are only 2,000 donor hearts are available each year.

 Therefore, heart transplant centers focus on assessing a given patient’s survival chances to determine their eligibility for a transplant. If eligible, they are placed on a waiting list for a transplant until a suitable donor heart is found. 

The biggest problem here is who will get the most recent donated organ ?

Or what is the chance of that specific person to live more than x years when he gets the given donated organ ?

These have been always a big issue for the decision makers.

The question is, How will we predict survival rates for a specific person?

Researchers have been dealing with this problems for many years , but analyzing either with the small sets of dataset with using conventional statistical techniques which does not take collinearity and the nonlinearity into account, 

Or they use some non-parametrical and non-statistical techniques that are computationally expensive and need prior knowledge about the data .

This is where data mining applications play a great role in Heart Transplantation Field of Medicine.

Data mining techniques reveal better and more accurate predictions for the survival of organ transplant recipients than any of the conventional methods used by previous studies.

and I will continue to explain more with the next blogs that I will post..........