Multi-feature weight factor extraction and survival risk assessment of hepatocellular carcinoma based on a clinical missing dataset-independent support vector machine

Abstract

Background

In clinical datasets, the characteristics of an individual patient vary so much that data loss becomes a normal event, which may be a unignorable dilemma in clinical data analysis. Therefore, the construction of a machine learning model aimed at missing clinical datasets (MCD) is of great clinical importance.

Methods

All included patients were divided into two groups according to outcome within a period of up to 36 months or less. The following characteristics (variables) were collected: age, sex, Child–Pugh status, hepatitis status, cirrhosis status, treatment, tumor size, portal vein tumor thrombus, and alpha fetoprotein (μg/mL), and a missing dataset-independent support vector machine (MDI-SVM) independent of missing data was built for the analysis.

Results

A MCD-independent SVM was developed based on clinical data from 1334 patients with hepatocellular carcinoma (HCC) at a single center, which had an accuracy of 84.43% in the survival analysis in the presence of 5% missing data. Based on the different combinations of features, our model calculated five features (tumor size, age, treatment, hepatitis status, and alpha fetoprotein) that had the greatest impact on survival in patients with HCC and extracted their weighting factors.

Conclusions

A MCD-independent SVM was developed to achieve prognosis prediction for patients with HCC in the absence of first-visit data.