Evaluation of risk factors and survival rates of patients with early-stage breast cancer with machine learning and traditional methods.

Background: This article is aimed to make predictions in terms of prognostic factors and compare prediction methods by using Cox proportional hazards regression analysis (CPH), some machine learning techniques and Accelerated Failure Time (AFT) model for post-treatment survival probabilities according to clinical presentations and pathological information of early-stage breast cancer patients.

Material And Methods: The study was carried out in three stages. In the first stage, the CPH method was applied.

Proposal and Validation of a Novel Scoring System for Hepatocellular Carcinomas Beyond Curability Borders.

Optimal scoring system for clinical prognostic factors in patients with unresectable hepatocellular carcinoma (HCC) is currently uncertain. We aimed to develop and externally validate an easy to use tool, particularly for this population, and named it the "unresectable hepatocellular carcinoma prognostic index" (UHPI). We evaluated the data of patients with treatment-naive unresectable HCC who were diagnosed in the training center from 2010 to 2019 (n = 209).