  Motivation: Thanks to digitization, we often have access to large databases,
consisting of various fields of information, ranging from numbers to texts and
even boolean values. Such databases lend themselves especially well to machine
learning, classification and big data analysis tasks. We are able to train
classifiers, using already existing data and use them for predicting the values
of a certain field, given that we have information regarding the other fields.
Most specifically, in this study, we look at the Electronic Health Records
(EHRs) that are compiled by hospitals. These EHRs are convenient means of
accessing data of individual patients, but there processing as a whole still
remains a task. However, EHRs that are composed of coherent, well-tabulated
structures lend themselves quite well to the application to machine language,
via the usage of classifiers. In this study, we look at a Blood Transfusion
Service Center Data Set (Data taken from the Blood Transfusion Service Center
in Hsin-Chu City in Taiwan). We used scikit-learn machine learning in python.
From Support Vector Machines(SVM), we use Support Vector Classification(SVC),
from the linear model we import Perceptron. We also used the
K.neighborsclassifier and the decision tree classifiers. Furthermore, we use
the TPOT library to find an optimized pipeline using genetic algorithms. Using
the above classifiers, we score each one of them using k fold cross-validation.
  Contact<EMAIL_ADDRESS>GitHub Repository:
https://github.com/ritabratamaiti/Blooddonorprediction
