Technological Model using Machine Learning Tools to Support Decision Making in the Diagnosis and Treatment of Pediatric Leukemia

In recent years, multiple applications of machine learning have been visualized to solve problems in different contexts, in which the health field stands out. That is why, based on what has been previously described, there is a wide interest in developing models based on machine learning for the creation of solutions that support medical assistance for disease such as pediatric cancer. Our work defines the proposal of a technological model based on machine learning which seeks to analyze the input medical data to obtain a predictive result, oriented to support the decision making of the specialist physician in relation to the diagnosis and treatment of pediatric leukemia. For the evaluation of the proposed model, a web validation system was developed that communicates with a service hosted on a cloud server which performs the predictive analysis of the inputs entered by the physician. As a result, an accuracy rate of 92.86% was obtained in the diagnosis of pediatric leukemia using the multiclass boosted decision tree classification algorithm.