A novel approach for accurate disease prediction: Application to heart and liver diseases
Satyanarayana Poojari, B. Ismail · 2025
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Description
Poojari and Ismail blend a decision tree with logistic regression to predict heart and liver disease in twenty twenty-five. Trees overfit by lumping all cases in a branch. Regression misclasses often in wide data. The hybrid aims to keep both strengths.
Simulations across sample sizes put the hybrid ahead of five rival models. Real data backed the claim, with top accuracy on both disease sets. One set was balanced, the other skewed, and the hybrid handled both. That frees users from hunting for balance fixes. Early flags could sharpen care choices, the authors argue.
Catalog record
| CREATOR | Satyanarayana Poojari, B. Ismail |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 31 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |