The objective of the experiment is to predict the medication adherence of a patient using binary classification algorithms
Medication non-adherence ,is an enormous burden to the world’s health care system. Approximately 125,000.deaths per year in the United States are linked to medication non-adherence.33 to 69 % of hospital admission leads to an estimation of $100-300 billion every year. How can we predict this behavior or prevent it in order to enact the adherence to medication? Medication adherence model predicts the patient's medication adherence using two class boosted decision tree algorithm Inputs: The input data is the simulated patient data set from a simulator tool with total of 10000 rows and 18 features. The features includes Social/economic factors,Provider-patient/health care system factors ,Condition-related factors ,Therapy-related factors ,Patient-related factors Outputs: The output of the model is the probability of medication adherence. The model predicts the probability that the given patient will become adherent or not.