Which model is NOT one of the four common models listed for healthcare prediction?

Which model is NOT one of the four common models listed for healthcare prediction?

• A. Support Vector Machines
• B. Neural networks
• C. Decision trees
• D. Naive Bayes

Answer: (A)

In healthcare prediction, a common set of modeling approaches is often highlighted as a quick reference. Neural networks, decision trees, Naive Bayes, and logistic regression are typically listed as the four. Support Vector Machines, while powerful, aren’t part of that standard quartet in this material, so they’re identified as not belonging to the four. The included models cover a range of strengths: neural networks handle complex, nonlinear patterns; decision trees are intuitive and easy to interpret; and Naive Bayes offers simple probabilistic reasoning. SVMs can be strong performers as well, but they sit outside that particular four in this context.

In healthcare prediction, a common set of modeling approaches is often highlighted as a quick reference. Neural networks, decision trees, Naive Bayes, and logistic regression are typically listed as the four. Support Vector Machines, while powerful, aren’t part of that standard quartet in this material, so they’re identified as not belonging to the four. The included models cover a range of strengths: neural networks handle complex, nonlinear patterns; decision trees are intuitive and easy to interpret; and Naive Bayes offers simple probabilistic reasoning. SVMs can be strong performers as well, but they sit outside that particular four in this context.