article · Journal Of Big Data
Hepatocellular carcinoma is a common form of liver cancer requiring precise predictive tools to enable early detection and treatment. Using a clinical dataset of liver cancer patients, an evaluation compared five machine learning algorithms before and after applying feature reduction methods. Techniques including feature weighting, correlation analysis, and optimised selection identified the most relevant clinical indicators, trimming high-dimensional data. Across decision trees, Naive Bayes, support vector machines, neural networks, and K-nearest neighbours, reducing the feature set consistently boosted predictive accuracy, precision, recall, and F-score while cutting execution time. The highest performance reached was 97.33 percent accuracy with Naive Bayes, while decision trees, neural networks, and support vector machines each achieved 96 percent accuracy, and K-nearest neighbours reached 94.67 percent.
Accurate early detection of hepatocellular carcinoma is critical for effective clinical intervention. By filtering clinical data to only the most relevant indicators, machine learning models run faster and make more accurate predictions. This demonstrates that data reduction can improve the reliability and computational efficiency of predictive models trained on complex clinical patient information.
The findings could inform the development of computational diagnostic support tools for clinical healthcare providers assessing liver cancer risk. The work represents early-stage algorithm testing and benchmarking on patient data, meaning substantial clinical validation and software integration would be required before translation into healthcare software products.
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Abstract Hepatocellular carcinoma (HCC) is a highly prevalent form of liver cancer that necessitates accurate prediction models for early diagnosis and effective treatment. Machine learning algorithms have demonstrated promising results in various medical domains, including cancer prediction. In this study, we propose a comprehensive approach for HCC prediction by comparing the performance of different machine learning algorithms before and after applying feature reduction methods. We employ popular feature reduction techniques, 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. Subsequently, we apply multiple algorithms, including Naive Bayes, support vector machines (SVM), Neural Networks, Decision Tree, and K nearest neighbors (KNN), to both the original high-dimensional dataset and the reduced feature set. By comparing the predictive accuracy, precision, F Score, recall, and execution time of each algorithm, we assess the effectiveness of feature reduction in enhancing the performance of HCC prediction models. Our experimental results, obtained using a comprehensive dataset comprising clinical features of HCC patients, demonstrate that feature reduction significantly improves the performance of all examined algorithms. Notably, the reduced feature set consistently outperforms the original high-dimensional dataset in terms of prediction accuracy and execution time. After applying feature reduction techniques, the employed algorithms, namely decision trees, Naive Bayes, KNN, neural networks, and SVM achieved accuracies of 96%, 97.33%, 94.67%, 96%, and 96.00%, respectively.
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DOI: 10.1186/s40537-024-00944-3
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