Mortality evaluation and life expectancy prediction of patients with Hepatocellular carcinoma with data minding

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Abstract Background: The complexity of systemic variables and comorbidities make it difficult to determine the best treatment for patients with hepatocellular carcinoma (HCC). It is impossible to perform a multidimensional evaluation of every patient, but guidelines based on analyses of said complexities would be the next best option. Traditional statistics are inadequate for developing predictive models with many variables; however, data mining is well-suited to the task. Patients and Methods and finding: The clinical profiles and data of a total of 537 patients diagnosed with Barcelona Clinic Liver Cancer stages B and C from 2009 to 2019 were retrospectively analyzed using 4 decision-tree algorithms. 19 treatments, 7 biomarkers, and 4 states of hepatitis were tested to see which combinations would result in survival times greater than a year. 2 of the algorithms produced complete models through single trees, which made only them suitable for clinical judgement. A combination of alpha fetoprotein ≤ 210.5 mcg/L, glutamic oxaloacetic transaminase ≤ 1.13 µkat/L, and total bilirubin ≤ 0.0283 mmol/L was shown to be a good predictor of survival > 1 year, and the most effective treatments for such patients were radio-frequency ablation (RFA) and transarterial chemoembolization (TACE) with radiation therapy (RT). In patients without this combination, the best treatments were RFA, TACE with RT and targeted drug therapy, and TACE with targeted drug therapy and immunotherapy. The main limitation of this study was small sample. With small sample size, we may developed a less reliable model system, failing to produce any clinically important results or outcomes Conclusion: Data mining can produce models to help clinicians predict survival time at the time of initial HCC diagnosis and then choose the most suitable treatment.

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