Healthcare Claims Prediction: Navigating the Impact of Outliers on Predictive Models Healthcare Claims Prediction: Navigating the Impact of Outliers on Predictive Models Predicting healthcare claims accurately is a cornerstone for insurance companies aiming to manage risks and set appropriate premiums. One significant challenge in this domain is dealing with outliers—data points that deviate substantially from the rest of the dataset. These anomalies, often resulting from rare events or data irregularities, can skew results and complicate predictive modeling. This article delves into the role of outliers in healthcare claims prediction, the methodologies for handling them, and the trade-offs involved in removing them.
Outliers are extreme data points that differ markedly from the rest of a dataset. They can emerge due to:
• Measurement errors: Data entry mistakes or technical glitches during data collection.
• Rare events: Uncommon but legitimate occurrences, such as catastrophic medical conditions leading to exorbitant claims.
- Z-Scores: Measures how far a value is from the Mean in terms of standard deviations. A threshold of ∣z∣ >3 is commonly used to flag outliers.
- Interquartile Range (IQR): Outliers fall outside [Q1−1.5×IQR,Q3+1.5×IQR], where Q1 and Q3 are the first and third quartiles.
- Percentile-Based Thresholds: Identifies outliers as values above the 95th percentile or below the 5th percentile.
In this study, z-scores with a threshold of ∣z∣>3 were employed, resulting in the identification and removal of 172 outliers (approximately 3.2% of the dataset).
Training and Validation Performance
Removing outliers improved the model's ability to capture general trends in the data:
• Validation Mean Absolute Error (MAE): 421.35
• Cross-Validation MAE: 441.43
• Validation Percentage Error: 1.21%
By focusing on "typical" claims, the model reduced prediction errors for the majority of the data, leading to enhanced precision during validation.
However, removing outliers had adverse effects on test set performance:
• Kaggle Test Score MAE: Increased dramatically to 1246.45.
• The test set included high value claims similar to those removed during training, causing the model to perform poorly on these data points. Visualization of Outliers
Box Plot of Total Claims by Total Severity:
• Higher severity levels are associated with higher total claims.
• Significant outliers exist across all severity levels, especially at higher levels, indicating substantial variability in claims for severe cases.
Scatter Plot of Age vs Total Claims:
• Total claims exhibit wide variation across ages, with no strong linear relationship.
• Outliers are visible across all age groups, particularly in the 75–80 age range.
- Loss of Representativeness: Removing outliers created a mismatch between the training data and the test set, which still contained similar extreme values.
- Model Bias: The model, trained without outliers, gravitated toward the mean and underestimated high-claim events.
- Real-World Implications: In healthcare, extreme claims often signify critical cases. Ignoring these values limits the model’s utility for real-world applications. Key Insights and Recommendations
• When focusing on modeling the central tendency of the data. • When outliers are due to data errors or are irrelevant to the prediction goal. • For use cases prioritizing low-error rates for typical data points, such as resource allocation.
• When outliers reflect significant, real-world phenomena (e.g., catastrophic medical events). • When the test or production data is expected to contain similar extreme values.
• Robust Models: Algorithms like XGBoost naturally handle outliers without removing them. • Log Transformations: Mitigate the impact of skewed data while retaining outliers. • Stratified Models: Separate models can be trained for high-value claims to improve prediction accuracy for extreme cases.
Outliers are a double-edged sword in predictive modeling. While their removal can improve training and validation performance, it often comes at the cost of reduced generalizability. In healthcare claims prediction, where high-value claims are pivotal, preserving and strategically handling outliers is critical. This study underscores the importance of aligning outlier treatment strategies with business objectives and data characteristics to ensure robust and reliable predictions.