This study investigates the effectiveness of feature reduction techniques in enhancing the performance of machine learning algorithms for predicting hepatocellular carcinoma (HCC). The authors compare the performance of various algorithms, including Naive Bayes, support vector machines (SVM), Neural Networks, Decision Tree, and K Nearest Neighbors (KNN), both on the original high-dimensional dataset and after applying feature reduction methods such as weighting features, hidden feature correlation, feature selection, and optimized selection. The experimental results, based on a comprehensive dataset of clinical features of HCC patients, demonstrate that feature reduction significantly improves the accuracy and execution time of all examined algorithms. Specifically, the reduced feature set consistently outperforms the original dataset in terms of prediction accuracy and execution time. After feature reduction, the algorithms achieved accuracies of 96%, 97.33%, 94.67%, 96%, and 96.00%, respectively. The study highlights the importance of feature reduction in improving the performance of HCC prediction models, which can lead to earlier diagnosis and more effective treatment.This study investigates the effectiveness of feature reduction techniques in enhancing the performance of machine learning algorithms for predicting hepatocellular carcinoma (HCC). The authors compare the performance of various algorithms, including Naive Bayes, support vector machines (SVM), Neural Networks, Decision Tree, and K Nearest Neighbors (KNN), both on the original high-dimensional dataset and after applying feature reduction methods such as weighting features, hidden feature correlation, feature selection, and optimized selection. The experimental results, based on a comprehensive dataset of clinical features of HCC patients, demonstrate that feature reduction significantly improves the accuracy and execution time of all examined algorithms. Specifically, the reduced feature set consistently outperforms the original dataset in terms of prediction accuracy and execution time. After feature reduction, the algorithms achieved accuracies of 96%, 97.33%, 94.67%, 96%, and 96.00%, respectively. The study highlights the importance of feature reduction in improving the performance of HCC prediction models, which can lead to earlier diagnosis and more effective treatment.