Ensemble Machine Learning Model to Predict Benefaction of an Individual in the Health Sector

Abstract

Ensemble methodis a machine learning technique that combines several base models in order to produce one optimal predictive model. This work aims to develop blood donor’s prediction model, for the management of the blood bank during emergency situations using ensemble method. The proposed model uses two supervised algorithms including multivariate regression and decision tree algorithms. An automated intelligent system is developed that learns from the data presented to the machine learning model to predict blood donors. The system is integrated with score allocation to the blood donors. A network of available ethical blood donors’ model has been developed which can be used in case of an emergency for an ailing person. Machine Learning techniques are used to find a perfect matched donor with respect to blood group, medical history and other demographics. A prioritized/ranked donor list based on their medical history, habits and other blood related metrics is generated to benefit the receivers. The ensemble methods used in this intelligent system helps in report generation facilitating medical experts and the society in decision making leading to increased number of donors.