A healthcare data analyst is evaluating model performance. The model correctly predicted 142 out of 150 diabetic cases and correctly ruled out 88 out of 100 non-diabetic cases. What is the models overall accuracy, rounded to the nearest whole percent? - Parker Core Knowledge
A healthcare data analyst is evaluating model performance. The model correctly predicted 142 out of 150 diabetic cases and correctly ruled out 88 out of 100 non-diabetic cases. What is the model’s overall accuracy, rounded to the nearest whole percent?
A healthcare data analyst is evaluating model performance. The model correctly predicted 142 out of 150 diabetic cases and correctly ruled out 88 out of 100 non-diabetic cases. What is the model’s overall accuracy, rounded to the nearest whole percent?
This hands-on assessment reflects a broader conversation in US healthcare: how increasingly sophisticated data models support early detection and precision medicine. With rising diabetes prevalence and growing investments in AI-driven diagnostics, understanding model performance is critical for clinicians and researchers alike. In this context, evaluating accuracy helps determine how reliably such tools can flag risk and reduce false positives—key steps toward actionable insights.
Understanding the Context
What Is Model Accuracy, and Why It Matters?
Accuracy is a fundamental measure used to evaluate classification models, representing the proportion of correct predictions out of all predictions made. It combines insight from both true positives (correctly predicted diabetics) and true negatives (correctly ruled-out non-diabetics). For healthcare models, accuracy alone offers partial visibility, but when balanced with other metrics, it reveals how effectively a tool supports clinical decision-making. With 142 correct diabetic predictions from 150 cases, and 88 correct non-diabetic exclusions from 100, the foundation for meaningful interpretation is clear.
Calculating Overall Accuracy: The Numbers Behind the Diagnostics
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Key Insights
To determine accuracy, add the correctly predicted diabetic cases and non-diabetic exclusions:
142 (true positives) + 88 (true negatives) = 230 correct predictions.
Total test cases: 150 + 100 = 250.
Accuracy = 230 / 250 = 0.92, or 92%.
Rounded to the nearest whole percent, the model’s overall accuracy is 92%. This figure reflects strong performance but invites deeper understanding—accuracy is most meaningful when viewed alongside the data distribution and other diagnostic benchmarks. While the model shows clear capability, particular attention must be given to class imbalance and context-specific performance.
Why This Result Is Gaining Attention in U.S. Healthcare
The US healthcare landscape is increasingly focused on predictive analytics to improve early intervention and reduce costs. When a model demonstrates 92% accuracy—especially in ruling out disease—it strengthens confidence in AI-assisted screening tools. This matters as providers seek ways to manage rising diabetes rates and avoid unnecessary testing, which benefits both patients and health systems. These metrics are not just numbers—they inform how care is personalized and resources allocated nationwide.
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How A Healthcare Data Analyst Evaluates Model Performance
Accuracy
