Machine learning could become a new weapon in the fight against Medicare fraud. Machine learning can be a useful tool in detecting Medicare fraud, according to a new study that can recover anywhere from $ 19 billion to $ 65 billion lost in fraud each year. Researchers at Florida Atlantic University's College of Engineering and Computer Science recently published the world's first study using Medicare Big data, machine learning, and advanced analytics to automate fraud detection. They tested six different machine learners on balanced and unbalanced data sets and eventually found that the RF100 Random Forest algorithm would be most effective in detecting potential cases of fraud. They found that unbalanced data sets are more than balanced data sets when scanning for fraud.

Machine learning could become a new weapon in the fight against Medicare fraud. Machine learning could become a useful tool in helping to detect Medicare fraud, according to a new study, potentially reclaiming anywhere from $19 billion to $65 billion lost to fraud each year. Researchers from Florida Atlantic University's College of Engineering and Computer Science recently published the world's first study using Medicare Part B data, machine learning and advanced analytics to automate fraud detection. They tested six different machine learners on balanced and imbalanced data sets, ultimately finding the RF100 random forest algorithm to be most effective at identifying possible instances of fraud. They also found that imbalanced data sets are more preferable than balanced data sets when scanning for fraud.

Machine Learning is touching almost all kinds of industries including the healthcare industry. Techniques in machine learning and artificial intelligence are covering the healthcare industry in an enormous way, including the Medicare vertical. With the graph of medical data growing exponentially, it is easier now to achieve great insights by machine learning methods. But on the flip side, it can have serious issues such as the susceptibility to commit Medicare frauds. In one instance in Uttar Pradesh, 21 people were infected with HIV from contaminated syringes by a fraudulent physician in the name of cheaper treatment.

Using a highly sophisticated form of pattern matching, researchers from Florida Atlantic University's College of Engineering and Computer Science are teaching "machines" to detect Medicare fraud. About $19 billion to $65 billion is lost every year because of Medicare fraud, waste, or abuse. Like the proverbial "needle in a haystack," human auditors or investigators have the painstaking task of manually checking thousands of Medicare claims for specific patterns that could indicate foul play or fraudulent behaviors. Furthermore, according to the U.S. Department of Justice, right now fraud enforcement efforts rely heavily on health care professionals coming forward with information about Medicare fraud. "The Effects of Varying Class Distribution on Learner Behavior for Medicare Fraud Detection With Imbalanced Big Data," published in the journal Health Information Science and Systems, uses big data from Medicare Part B and employs advanced data analytics and machine learning to automate the fraud detection process.

IMAGE: This is Taghi M. Khoshgoftaar, Ph.D., co-author and Motorola Professor in FAU's Department of Computer and Electrical Engineering and Computer Science. Using a highly sophisticated form of pattern matching, researchers from Florida Atlantic University's College of Engineering and Computer Science are teaching "machines" to detect Medicare fraud. About $19 billion to $65 billion is lost every year because of Medicare fraud, waste or abuse. Like the proverbial "needle in a haystack," human auditors or investigators have the painstaking task of manually checking thousands of Medicare claims for specific patterns that could indicate foul play or fraudulent behaviors. Furthermore, according to the U.S. Department of Justice, right now fraud enforcement efforts rely heavily on health care professionals coming forward with information about Medicare fraud.