# Predicting the Survival of Primary Biliary Cholangitis Patients
###### Abstract  
Primary Biliary Cholangitis, which is thought to be caused by a combination of genetic and environmental factors, is a slow-growing chronic autoimmune disease in which the human body's immune system attacks healthy cells and tissues and gradually destroys the bile ducts in the liver. A reliable diagnosis of this clinical condition, followed by appropriate intervention measures, can slow the damage to the liver and prevent further complications, especially in the early stages. Hence, the focus of this study is to compare different classification Data Mining techniques, using clinical and demographic data, in an attempt to predict whether or not a Primary Biliary Cholangitis patient will survive. Data from 418 patients with Primary Biliary Cholangitis, following the Mayo Clinic's research between 1974 and 1984, were used to predict patient survival or non-survival using the Cross Industry Standard Process for Data Mining methodology. Different classification techniques were applied during this process, more specifically, Decision Tree, Random Tree, Random Forest, and Naive Bayes. The model with the best performance used the Random Forest classifier and Split Validation with a ratio of 0.8, yielding values greater than 93% in all evaluation metrics. With further testing, this model may provide benefits in terms of medical decision support.  
classification; data mining; predictive models; primary biliary cholangitis +
Footnote †: journal: Medical  
about the patient's status, i.e., if the patient was alive, in need of a liver transplant, or deceased, this study focused on the prediction of the survival of patients diagnosed with PBC using Data Mining (DM) techniques.  
DM is useful for analysing and exploring large datasets in order to discover meaningful patterns and rules [7]. The implementation of DM techniques can also facilitate the reduction in medical errors, enhance patient safety, standardise clinical practice, and improve patient outcomes [8]. Since the dataset is labelled, this study will use supervised techniques, which can include regression and classification [9]. As expected, and as previously stated, the focus of this paper will be on classification techniques, with different classifiers from the RapidMiner software being used to predict PBC patients' status, i.e., the label attribute, which corresponds to the attribute that predicts the patients' survival or non-survival. With this study, it will be possible to identify which patients have a greater chance of survival. As a result, hospitals and healthcare professionals will be able to concentrate their efforts, time, resources, and treatment options on patients who are more likely to survive. On the other hand, in terms of research, the cases of patients classified as patients who will not survive can be used for the search and development of innovative treatments.  
The paper is structured in five sections. After introducing the problem, it will be presented the Related Work Section 2, regarding works related to the PBC topic. This section is followed by a detailed description of the DM process carried out using the CRossIndustry Standard Process for Data Mining (CRISP-DM) methodology in the Materials and Methods Section 3. Next, an analysis of the obtained results and its interpretation is performed in the Discussion Section 4. Finally, the Conclusions Section 5, summarises the whole process, draws the final conclusions based on the results achieved, and presents a brief proposal for future work.