Uses And Trends In Hematopoietic Cell Transplantation In The United States of America:A Machine Learning Review

Artificial Intelligence(AI) based algorithm and various machine learning algorithm are used in healthcare successfully to enhance human life. The Hematopoietic Cell Transplantation (HCT)data set are analyzed for finding out the clinical outcomes and its types. Hematopoietic cell transplantation is a well therapeutic treatment for a variety of different diseases. The number of HCT operations conducted in the United States from 2014 to 2018, as well as the trends and results of HCT as reported to the CIMBTR, are evaluated and documented in this research. The analysis observes an increase in total transplants carried out in the United States of America. An increase of 13% was found from the year 2014 to 2018. The analysis is of transplants done are categorized based on donor type, age stem cell source, indications and states in the US. The most frequent indication was found to be Multiple myeloma (34% of all HCT). Also, the trends and increase in the transplant rates over the years 2014 to 2018 are also reported. This paper also provides a comparison analysis, development over the years and perspective on the transplant activity in the United States based on donors, indications and cell source. Various studies have been performed to predict HCT outcomes like survival, relapse using ML techniques like Regression, Bayesian learning ensemble learning and Support Vector Machine. The aim of the paper is to review the existing machine learning application and to provide various research direction for developing various Data Analytics tools.