This study investigates the effectiveness of feature reduction techniques in enhancing the performance of machine learning algorithms for hepatocellular carcinoma (HCC) prediction. The research compares the performance of various machine learning algorithms, including Naive Bayes, Support Vector Machines (SVM), Neural Networks, Decision Tree, and K-Nearest Neighbors (KNN), on both the original high-dimensional dataset and a reduced feature subset. The study employs feature reduction methods such as weighting features, hidden features correlation, feature selection, and optimized selection to extract a reduced feature subset that captures the most relevant information related to HCC. The results show that feature reduction significantly improves the performance of all examined algorithms, with the reduced feature set consistently outperforming the original high-dimensional dataset in terms of prediction accuracy and execution time. After applying feature reduction techniques, the algorithms achieved accuracies of 96%, 97.33%, 94.67%, 96%, and 96.00% for decision trees, Naive Bayes, KNN, neural networks, and SVM, respectively. The study highlights the effectiveness of feature reduction in boosting the performance of various AI techniques for HCC prediction. The research also addresses the limitations of traditional predictive models, such as the 'dimensionality curse', and proposes a comprehensive approach to enhance the accuracy and efficiency of HCC prediction models. The study contributes to the field of computational HCC prediction by comparing the performance of widely used machine learning algorithms before and after the implementation of feature reduction techniques. The findings suggest that feature reduction methods can be effectively employed in HCC prediction models, leading to improved accuracy and faster execution times. The study also discusses the potential of machine learning algorithms combined with feature reduction techniques for the early diagnosis and effective treatment of HCC, ultimately improving patient outcomes. Future work includes integrating multimodal data, training models on large, geographically diverse datasets, and developing models that can incorporate longitudinal data to predict risk changes and identify high-risk patients earlier.This study investigates the effectiveness of feature reduction techniques in enhancing the performance of machine learning algorithms for hepatocellular carcinoma (HCC) prediction. The research compares the performance of various machine learning algorithms, including Naive Bayes, Support Vector Machines (SVM), Neural Networks, Decision Tree, and K-Nearest Neighbors (KNN), on both the original high-dimensional dataset and a reduced feature subset. The study employs feature reduction methods such as weighting features, hidden features correlation, feature selection, and optimized selection to extract a reduced feature subset that captures the most relevant information related to HCC. The results show that feature reduction significantly improves the performance of all examined algorithms, with the reduced feature set consistently outperforming the original high-dimensional dataset in terms of prediction accuracy and execution time. After applying feature reduction techniques, the algorithms achieved accuracies of 96%, 97.33%, 94.67%, 96%, and 96.00% for decision trees, Naive Bayes, KNN, neural networks, and SVM, respectively. The study highlights the effectiveness of feature reduction in boosting the performance of various AI techniques for HCC prediction. The research also addresses the limitations of traditional predictive models, such as the 'dimensionality curse', and proposes a comprehensive approach to enhance the accuracy and efficiency of HCC prediction models. The study contributes to the field of computational HCC prediction by comparing the performance of widely used machine learning algorithms before and after the implementation of feature reduction techniques. The findings suggest that feature reduction methods can be effectively employed in HCC prediction models, leading to improved accuracy and faster execution times. The study also discusses the potential of machine learning algorithms combined with feature reduction techniques for the early diagnosis and effective treatment of HCC, ultimately improving patient outcomes. Future work includes integrating multimodal data, training models on large, geographically diverse datasets, and developing models that can incorporate longitudinal data to predict risk changes and identify high-risk patients earlier.