June 18, 2024Journal Of Big DataOpen Access

Feature reduction for hepatocellular carcinoma prediction using machine learning algorithms

View Full Paper

Ask AI

Bookmark

Feature reduction significantly improves prediction accuracy across machine learning models, outperforming high-dimensional clinical hepatocellular carcinoma datasets.
Naive Bayes achieved 97.33% accuracy with feature selection, whereas support vector machines reached 96.00% across the evaluated hepatocellular carcinoma cohort.
Comparative analysis of decision tree and neural networks classifiers demonstrates lower execution time, highlighting feature reduction for real-time cancer detection.

Discussion

No takes yet. Share an insight, caveat, or question.

Implication

Comparative evaluation demonstrates enhanced predictive accuracy across machine learning models in hepatocellular carcinoma patients, indicating the utility of feature reduction.

Cite This Study

Mostafa et al. (2024) studied this question.

synapsesocial.com/papers/68e642a9b6db6435875d4a2c https://doi.org/10.1186/s40537-024-00944-3

View Full Paper

Ask AI

Bookmark

Implication

Comparative evaluation demonstrates enhanced predictive accuracy across machine learning models in hepatocellular carcinoma patients, indicating the utility of feature reduction.

Feature reduction for hepatocellular carcinoma prediction using machine learning algorithms

Key Points

Discussion

Implication

Cite This Study

Feature reduction for hepatocellular carcinoma prediction using machine learning algorithms

Key Points

Discussion

Implication

Cite This Study

Also Consider

Also Consider