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Healthcare fraud detection prediction
- Healthcare fraud is a significant issue involving illegal activities like billing for services not provided or misrepresenting services.
- Machine learning offers an effective solution for automating the detection of healthcare fraud, reducing costs and improving efficiency.
- A capstone project focused on detecting potential fraud in healthcare claims through machine learning techniques is discussed.
- The project involves analyzing historical data to identify patterns distinguishing fraudulent activities from legitimate claims.
- Key steps in the project include data preprocessing, feature engineering, and model evaluation using metrics like precision and recall.
- The dataset used in the project includes Inpatient, Outpatient, Beneficiary, and Fraud labels data uploaded to Kaggle by Rohit Anand Gupta.
- Key findings from the project include insights on inpatient stay duration, claim duration, reimbursed amount analysis, and numerical variables analysis.
- Different machine learning models like Logistic Regression, Decision Tree, XGBoost, and CatBoost were evaluated for fraud detection performance.
- The CatBoost model demonstrated high performance in distinguishing fraudulent claims, with a focus on precision, recall, and ROC-AUC metrics.
- Recommendations for optimizing fraud detection models include addressing class imbalance, threshold adjustment, and combining hybrid approaches for enhanced performance.
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