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  4. Vol 5, Issue 5, 2024

Enhancing Post-Hepatectomy Patient Care: Addressing Data Gaps in Liver Cancer Prognosis

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DOI: 10.23977/medsc.2024.050515 | Downloads: 12 | Views: 500

Author(s)

Ziyan Fang 1

Affiliation(s)

1 University of Washington, Seattle, WA 98195, USA

Corresponding Author

Ziyan Fang

ABSTRACT

Liver cancer is one of the most complex diseases to treat and monitor, with post-hepatectomy patient care being critical due to the liver's essential metabolic and detoxification functions. Post-operative complications, including hepatitis and cirrhosis, are significant indicators of patient prognosis and survival outcomes. However, clinical studies often suffer from substantial data gaps, particularly regarding complications, which can distort analyses and recommendations. This research aims to address these gaps by employing advanced machine learning techniques to impute missing data in post-hepatectomy datasets. Using Random Forest, Bagging, and Boosting algorithms, we segmented patients into distinct clusters and predicted missing values with improved accuracy. Our findings demonstrate that the Boosting approach outperforms Bagging in terms of precision, and our imputation model achieves an impressive ROC score of 86%, indicating high diagnostic ability. The improved dataset reveals previously obscured patterns, such as correlations between pre-operative liver function and post-operative complications, offering valuable clinical insights. This study highlights the potential of machine learning to enhance healthcare data analysis and inform more accurate, data-driven decision-making in liver cancer post-operative care.

KEYWORDS

Liver Cancer; Post-hepatectomy Care; Machine Learning; Data Imputation; Predictive Modeling

CITE THIS PAPER

Ziyan Fang. Enhancing Post-Hepatectomy Patient Care: Addressing Data Gaps in Liver Cancer Prognosis. MEDS Clinical Medicine (2024) Vol. 5: 104-110. DOI: http://dx.doi.org/10.23977/medsc.2024.050515.

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