Machine learning model to identify prognostic factors in glioblastoma: a SEER-based analysis

Abstract

Purpose: Analyzing and interpreting large amounts of complex health care data are becoming more insufficient by traditional statistical approaches. However, analyzing Big Data (BD) by machine learning (ML) supports the storage, classification of patient information. Therefore, improves disease identification, treatment evaluation, surgical planning, and outcome prediction. The current study aims to create a competing risk model to identify prognostic factors in glioblastoma (GB). Materials and methods: The study included 31663 patients diagnosed with GB between 2007 and 2018. The data in the study were taken from the Surveillance, Epidemiology, and End Results (SEER) database. Overall survivals (OS), age, race, gender, primary site, laterality, surgery and tumor size at the time of diagnosis, vital status, and follow-up time (months) were selected for the analyzes. Results: The median OS of the patients was found to be 9.00±0.09 months. In addition, all variables in the table were statistically significant risk factors for survival except gender. Therefore, surgery, age, laterality, primary site, tumor size, race, gender variables were used as independent risk factors, and vital status was used as a dependent variable for ML analysis. Looking at the ML results, hybrid model gave the best results according to Accuracy, F-measure, and MCC performance criteria. According to hybrid model, which has the best performance, the diagnosis of alive/dead in 84 and 74 out of 100 patients can be interpreted as correct for 1- and 2-year, respectively. Conclusions: The model created by ML was 84.9% and 74.1% successful in predicting 1- and 2-year survival in GB patients, respectively. Recognition of the fundamental ideas will allow neurosurgeons to understand BD and help evaluate the extraordinary amount of data within the associated healthcare field.