Healthcare fraud types by providers are:
Billing for services that were not provided. Duplicate submission of a claim for the same service. Misrepresenting the service provided. Charging for a more complex or expensive service than was actually provided. Billing for a covered service when the service actually provided was not covered. The datasets consists of
Train.csv and test.csv-
This file consists of Provider Data , Inpatient Data, Outpatient Data, Beneficiary Details Data and various categorical features and also the PotentialFraud which we have to predict.
Provider Data
This data provides insights of Provider Id , PotentialFraud mark on that ids. we have to predict PotentialFraud for future data.
Inpatient Data
This data provides insights about the claims filed for those patients who are admitted in the hospitals. It also provides additional details like their admission and discharge dates and admit d diagnosis code.
Outpatient Data
This data provides details about the claims filed for those patients who visit hospitals and not admitted in it.
Beneficiary Details Data
This data contains beneficiary KYC details like DOB, DOD, Gender, Race, health conditions (Chronic disease if any), State, Country they belong to etc.
The goal of this project is to " predict the potentially fraudulent providers " based on the claims filed by them.along with this, we will also discover important variables helpful in detecting the behaviour of potentially fraud providers. This is basically a classification problem.