Skin cancer is one of the most common cancers in the world. However, the disease is
curable if detected in the beginning stage. Early detection of malignant lesions through …

The problem of class imbalance has always been considered as a significant challenge to
traditional machine learning and the emerging deep learning research communities. A …

Due to the size of the data involved, performance is an important consideration in the task of
detecting fraudulent Medicare insurance claims. We evaluate CatBoost and XGBoost on the …

Class label noise is a critical component of data quality that directly inhibits the predictive
performance of machine learning algorithms. While many data-level and algorithm-level …

In this study we investigate the performance of CatBoost in the task of identifying Medicare
fraud. The Medicare claims data we use as input for CatBoost contain a number of …

Many natural language processing (NLP) tasks are naturally imbalanced, as some target
categories occur much more frequently than others in the real world. In such scenarios …

Objective: Identifying fraud in healthcare programs is crucial, as an estimated 3%–10% of
the total healthcare expenditures are lost to fraudulent activities. This study presents a …

Advances in data mining and machine learning continue to transform the healthcare industry
and provide value to medical professionals and patients. In this study, we address the …

Classification models serve as effective tools for Medicare fraud detection, but their
performance can be influenced by a number of factors. This paper focuses on addressing …

Due to the recent escalation in the amount of text data available and used online, text
classification has become a staple for data analysts when extracting relevant information …