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Running breast cancer diagnosis — malignancy drivers analysis...
Sent to . Inside: interactive charts, statistical results, R code, and AI insights.
Analyze another fileLogistic regression is fitted with diagnosis (malignant vs benign) as the binary outcome and all 30 cell-nucleus measurements (mean, standard error, worst values for radius, texture, perimeter, area, smoothness, compactness, concavity, concave points, symmetry, and fractal dimension) as inputs. Odds ratios with 95% confidence intervals rank each feature's independent contribution to malignancy risk. A random forest model provides a scale-invariant feature importance ranking. Visualizations contrast the distributions of the strongest predictors (e.g., radius_mean) between benign and malignant cases.
Interactive bar visualization
Interactive table visualization
Interactive box visualization
Interactive bar visualization
Plain-English interpretation of what the numbers mean, what's significant, and what to do next.
See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.
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