# Predicting the Survival of Primary Biliary Cholangitis Patients
## 2 Related Work  
The subject of this paper has been mentioned and studied in several articles, given the fact that it is not only a chronic liver disease but also a rare condition that is often only treated through liver transplantation. As a result, the majority of the studies in the literature are devoted to improving treatment management and evaluating the influence of certain substances in disease spread.  
Reference [10] studied the progression of PBC in 312 patients treated at the Mayo Clinic between January 1974 and May 1984. This study was based on repeated patient visits (a total of 1945 patient visits) that were important to evaluate the changes in the prognostic variables of PBC, such as age, albumin value, prothrombin time, bilirubin value, and edema. This dataset is similar to the one used in this study but not exactly the same. Using these data and the Cox proportional-hazards regression model, it was built an updated model to be used in the short-term survival prediction at any stage of the disease. It was concluded that the new updated model makes more accurate predictions in terms of short-term survival than the original Mayo model, meaning that the Mayo model is still the best choice for predicting the patient's survival for more than 3 years, but for periods up to 2 years, this new model offers a more accurate estimation of PBC patients' survival.  
The biochemical response to invodeoxycholic acid (UDCA) in PBC was analysed by [11] and is related to the long-term prognosis of the disease, thus allowing the identification of the patients' need of new therapeutic approaches. The aim of this study was to determine, in a population of patients in an early-stage of the disease, the most efficient biochemical response to UDCA, allowing the prediction the absence of poor outcome, as described by liver-related death, liver transplantation, complications of cirrhosis, or histological evidence of cirrhosis development.  
A similar study was developed by [12], in which it was explored the effect of azadi-thioprine on the survival of PBC patients. Using a randomised clinical trial containing 248 patients, where the immunosuppressive medication azathioprine was given to 127 of those patients, with the remaining 121 patients receiving placebo, which has no therapeutic value. Similarly to the approach taken by [10], it was applied a Cox multiple regression analysis and an adjustment relative to the imbalance between the treatment groups. The therapeutic effect of azathioprine has been shown to reduce the risk of non-survival to 59% of those observed with placebo, as well as to improve the survival time by up to 20 months for the average patient, revealing a statistical significance of azathioprine therapy.  
In a global manner, as it can be seen from the previous articles, there is more emphasis in the literature on the analysis of treatments for PBC and not specifically on its prognosis or evolution. Thus, more studies are needed to broaden the knowledge and understanding on the diagnosis of this condition as early as possible [13]. Machine learning algorithms can be used to perform early diagnosis and risk stratification, as well. Hence, developing diagnostic algorithms for PBC patients based on demographic variables, symptomatology, and laboratory results is a resourceful tool for improving the quality of clinical practice. We present a novel DM approach for PBC where the emphasis is the use of classified data to predict the survival of patients diagnosed with PBC. Several experiments were carried out, in which different DM techniques and feature selection setups were taken into account. Such knowledge is particularly useful to perform early diagnosis and risk stratification, thus improving the quality of clinical practice and lowering the mortality rate of patients diagnosed with PBC.