How to Analyze Features Using Yellowbrick

How to Analyze Features Using Yellowbrick, Detecting healthcare fraud poses unique challenges, particularly when navigating claims data.

The journey often involves bridging individual transactions to perform a comprehensive provider-level analysis.

How to Analyze Features Using Yellowbrick

In this article, we will explore methods to aggregate claims data to develop valuable provider features, utilize Yellowbrick’s Parallel Coordinates for visualization, and discuss additional visualization tools to enhance feature analysis.

By integrating robust data practices with visual analytics, we can uncover critical patterns that differentiate fraudulent healthcare providers from legitimate ones.

Understanding Healthcare Fraud Detection: The Dataset

To effectively tackle healthcare fraud detection, we utilize a comprehensive dataset comprising three key components:

• Train Dataset (5,410 rows): Contains provider IDs alongside fraud labels.
• Train Inpatient Dataset (40,474 rows): Comprises claims related to hospital admissions.
• Train Outpatient Dataset (517,737 rows): Encompasses claims for outpatient visits.

The primary challenge arises from having the target variable—fraud occurrence—at the provider level, while the bulk of usable information resides within individual claims spread across multiple tables.

This dissonance necessitates careful aggregation of the data.

Aggregating Claims Data for Provider-Level Insights

To create initial provider-level features, we first identify total claims amounts. Here’s how to approach the task programmatically using Python’s Pandas library:

import pandas as pd

# Load datasets
train = pd.read_csv('Train-1542865627584.csv')  # Provider fraud labels
inpat = pd.read_csv('Train_Inpatientdata-1542865627584.csv')  # Inpatient claims
outpat = pd.read_csv('Train_Outpatientdata-1542865627584.csv')  # Outpatient claims

# Calculate provider-level inpatient claims totals
inpatient_totals = inpat.groupby('Provider')['InscClaimAmtReimbursed'].sum().reset_index()
inpatient_totals = inpatient_totals.rename(columns={'InscClaimAmtReimbursed': 'IP_Claims_Total'})

# Calculate provider-level outpatient claims totals
outpatient_totals = outpat.groupby('Provider')['InscClaimAmtReimbursed'].sum().reset_index()
outpatient_totals = outpatient_totals.rename(columns={'InscClaimAmtReimbursed': 'OP_Claims_Total'})

The first step in our aggregation process involves summing the claim amounts for each provider, distinguishing between inpatient and outpatient claims. Given that not all providers manage both types of claims, we take care to merge the datasets effectively:

# Merge inpatient and outpatient totals
provider_claims = pd.merge(
    inpatient_totals,
    outpatient_totals,
    on='Provider',
    how='outer'
).fillna(0)  # Fill missing values with 0 for providers with only one type of claims

# Merge with fraud labels
final_df = pd.merge(
    provider_claims,
    train[['Provider', 'PotentialFraud']],
    on='Provider',
    how='outer'
)

print(final_df)

With our final dataset, we can now evaluate total claim amounts for each provider, segmenting by inpatient and outpatient services. This approach reveals intriguing trends, such as providers flagged for potential fraud often exhibiting higher claim amounts across both categories.

Visualizing Provider Claims Through Parallel Coordinates

One of the most effective ways to visualize the data and discern patterns is through a Parallel Coordinates plot. This powerful tool illustrates how various features relate to our target variable in a multi-dimensional landscape.

from yellowbrick.features import ParallelCoordinates
from sklearn.preprocessing import StandardScaler

# Preparing the features and target
X = final_df[['IP_Claims_Total', 'OP_Claims_Total']]
y = final_df['PotentialFraud']

# Scaling the features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
X_scaled = pd.DataFrame(X_scaled, columns=X.columns)

# Create Parallel Coordinates visualization
visualizer = ParallelCoordinates()
visualizer.fit_transform(X_scaled, y)
visualizer.show()

Insights from Parallel Coordinates

The visualization created through the Parallel Coordinates plot provides several key insights:

1. Pattern Separation: Fraudulent providers typically show more extreme claims patterns across both inpatient and outpatient services.
2. Line Slopes: A marked difference is observed in the slopes of lines connecting claims amounts, indicating varied claiming behaviors between fraudulent and non-fraudulent providers.
3. Density Distribution: Non-fraudulent providers predominantly cluster in lower claims amounts, presenting a clear contrast to their fraudulent counterparts.
4. Feature Scaling: Using StandardScaler allows us to visualize patterns more effectively by standardizing the feature scales.

Exploring Yellowbrick’s Visualization Tools for Feature Analysis

While our focus here has been on Parallel Coordinates, Yellowbrick provides a robust suite of feature analysis tools tailored for different analytical needs:

• Rank Features: Evaluate feature importance through correlation and covariance metrics.
• PCA Projection: Project high-dimensional data onto a 2D space, preserving maximum variance.
• Manifold Visualization: Apply advanced dimensionality reduction techniques while preserving local data relationships.
• RadViz Visualizer: Display features in a circular plot to identify class separation patterns effectively.
• Jointplots: Combine scatter plots with detailed distribution information for insightful feature-target relationships.

Choosing the Right Visualization

Your choice of visualization should align with your analytical objectives. For instance, Rank Features is ideal for feature selection, while PCA is best for dimensionality reduction.

RadViz and Parallel Coordinates are excellent for exploring class separation patterns, while Jointplots are beneficial for investigating relationships among continuous variables.

Conclusion

Integrating thoughtful data practices with visual analytics significantly enhances our ability to detect healthcare fraud.

By aggregating claims data at the provider level and leveraging visualization tools like Yellowbrick, we can uncover critical patterns that signal potential fraudulent behavior.

As we’ve seen, the right visualization choice can transform complex data into meaningful insights, enabling more effective healthcare fraud detection strategies.

Model Performance with Yellowbrick » FINNSTATS