# Technical Document Extraction: Sepsis Prediction Model (Test) Confusion Matrix ## 1. Document Overview This image is a **Confusion Matrix** representing the performance of a "Sepsis Prediction Model" on a test dataset. It visualizes the relationship between the actual ground truth (True label) and the model's classifications (Predicted label). ## 2. Component Isolation ### Header * **Title:** Sepsis Prediction Model (Test) ### Main Chart (Heatmap) The chart is a 2x2 grid. The color intensity represents the normalized proportion of predictions within each true class row. * **Y-Axis (True label):** Categories are "sepsis" (top) and "no sepsis" (bottom). * **X-Axis (Predicted label):** Categories are "sepsis" (left) and "no sepsis" (right). ### Legend (Color Bar) * **Location:** Right side of the chart. * **Type:** Gradient scale (light blue to dark blue). * **Range:** Approximately 0.2 to 0.8. * **Function:** Indicates the normalized ratio of the row total. Darker blue indicates a higher percentage of correct or incorrect classifications relative to that specific true label. ## 3. Data Table Reconstruction The following table transcribes the numerical data and labels found within the four quadrants of the matrix. | True Label \ Predicted Label | sepsis (Predicted) | no sepsis (Predicted) | Row Totals (Calculated) | | :--- | :--- | :--- | :--- | | **sepsis (True)** | **TP (True Positive)**<br>Count: 286<br>Percentage: 64.13% | **FN (False Negative)**<br>Count: 160<br>Percentage: 35.87% | 446 | | **no sepsis (True)** | **FP (False Positive)**<br>Count: 81,098<br>Percentage: 17.19% | **TN (True Negative)**<br>Count: 390,794<br>Percentage: 82.81% | 471,892 | ## 4. Trend and Data Analysis ### Trend Verification * **True Sepsis Row:** The "TP" cell is a medium-dark blue, while the "FN" cell is a light blue. This indicates the model is more likely to correctly identify sepsis than to miss it when it is present. * **True No Sepsis Row:** The "TN" cell is the darkest blue in the entire chart, while the "FP" cell is nearly white. This indicates a very high success rate in identifying the absence of sepsis, though the raw number of false positives is high due to class imbalance. ### Key Performance Metrics (Derived from Data) * **Sensitivity (Recall):** 64.13% (The model correctly identifies roughly 2/3 of actual sepsis cases). * **Specificity:** 82.81% (The model correctly identifies over 4/5 of non-sepsis cases). * **Class Imbalance:** There is a massive disparity in the dataset size. There are only 446 "sepsis" instances compared to 471,892 "no sepsis" instances. * **False Positive Impact:** While the FP rate is relatively low (17.19%), because the "no sepsis" population is so large, the model generated 81,098 false alarms, which significantly outweighs the 286 true positives.

# Sepsis Prediction Model (Test) Confusion Matrix Analysis ## Key Components - **Title**: "Sepsis Prediction Model (Test)" - **Axes**: - **X-axis (Predicted label)**: - Categories: "sepsis", "no sepsis" - **Y-axis (True label)**: - Categories: "sepsis", "no sepsis" - **Legend**: - Color bar ranging from **0.2** (light blue) to **0.8** (dark blue), representing prediction confidence or proportion. ## Data Structure The matrix is divided into four quadrants, each representing a classification outcome: 1. **Top-left (TP - True Positive)**: - **Count**: 286 - **Percentage**: 64.13% (of true sepsis cases) 2. **Top-right (FN - False Negative)**: - **Count**: 160 - **Percentage**: 35.87% (of true sepsis cases) 3. **Bottom-left (FP - False Positive)**: - **Count**: 81,098 - **Percentage**: 17.19% (of true no sepsis cases) 4. **Bottom-right (TN - True Negative)**: - **Count**: 390,794 - **Percentage**: 82.81% (of true no sepsis cases) ## Color Scale Verification - **Legend Position**: Right side of the matrix. - **Color Matching**: - **TN (dark blue)**: 0.8281 (matches darkest shade). - **FP (light blue)**: 0.1719 (matches lightest shade in "no sepsis" row). - **TP (medium blue)**: 0.6413 (intermediate shade). - **FN (light blue)**: 0.3587 (intermediate shade in "sepsis" row). ## Trend Analysis - **True Negatives (TN)**: Dominates the matrix (82.81%), indicating the model correctly predicts "no sepsis" most frequently. - **False Positives (FP)**: High count (81,098) despite low percentage (17.19%), suggesting class imbalance (fewer true sepsis cases). - **True Positives (TP)**: Moderate performance (64.13% of sepsis cases correctly identified). - **False Negatives (FN)**: 35.87% of sepsis cases missed, highlighting potential risks in clinical settings. ## Spatial Grounding - **Legend**: Right-aligned, vertical color bar. - **Cell Colors**: - **TN**: Dark blue (highest value). - **FP**: Light blue (lowest value in "no sepsis" row). - **TP**: Medium blue (intermediate value in "sepsis" row). - **FN**: Light blue (intermediate value in "sepsis" row). ## Critical Observations 1. **Class Imbalance**: - True no sepsis cases (TN + FP = 471,892) vastly outnumber true sepsis cases (TP + FN = 446). 2. **Model Bias**: - High TN rate (82.81%) suggests the model is overly optimized for the majority class ("no sepsis"). 3. **Clinical Implications**: - High FN rate (35.87%) could lead to missed sepsis diagnoses, emphasizing the need for improved sensitivity. ## Data Table Reconstruction | True Label \ Predicted Label | sepsis | no sepsis | |------------------------------|--------------|--------------| | **sepsis** | TP: 286 (64.13%) | FN: 160 (35.87%) | | **no sepsis** | FP: 81,098 (17.19%) | TN: 390,794 (82.81%) | ## Conclusion The model performs well in identifying "no sepsis" cases but struggles with sensitivity for sepsis detection. The high false positive rate in the "no sepsis" category may reflect dataset imbalance, while the false negative rate highlights a critical area for improvement in clinical applications.