A Review Paper on Classification of Stem Cell Transplant to Identify the High Survival Rate

Abstract

A patient undergoing hematopoietic stem cell transplant faces various risk factors and has become the standard of care for congenital or acquired disorders of the hematopoietic system or with chemo-sensitive, radiosensitive or immunosensitive malignancies. Analyzing and classifying the data from past transplant can enhance the understanding of the factors leading to highest survival rates among the patients. Over the last few decades there has been tremendous use of technology in this field. Stem cell transplant remains a dangerous procedure as it requires significant infrastructure and a network of specialists from all fields of medicine. In this paper, we are using a classification algorithm known as Support Vector Machine to classify the patients who have undergone stem cell transplant with high odds of survival. We are also keeping track of information about the donors within the family and outside the family which has a direct impact in the prioritization of resources. Classification of this information is useful to create the need for a global perspective for all cell, tissue, and organ transplants and to reveal statistical structure with potential implications in evidence-based prioritization of resources. Machine-learning techniques proved useful in analyzing the correct data from various datasets as this techniques were previously been considered too complex to analyze.
DOI: 10.17762/ijritcc2321-8169.160435