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Background: Breast cancer is the most common type of cancer amongst women worldwide. Considering its high incidence, effective detection and prognosis of this type of cancer may have a significant effect on reducing expenditures. In this study, we propose a model to predict the 60-month survivability in patients with breast cancer and investigate the effects of each feature on the obtained model. Methods: We base this model on the information gathered by the Breast Disease Research Center, Shiraz University of Medical Sciences, Shiraz, Iran from 5673 patients with breast cancer. The goal of this study was to predict breast cancer survivability at early diagnosis, so the features used in the research are among those considered affordable, specifically at the initial steps of diagnosis. After preprocessing all of the cases and features, we constructed this model based on 1930 cases and 16 of their associated features using logistic regression method. The model then was evaluated with 10-fold cross validation. Results: Based on all subsets of the 16 features, we evaluated numerous models. We selected a model that achieved the best sensitivity and specificity, and used fewer features as the best model. We considered this model for further analysis, which is consisted of following features: age at the time of diagnosis, type of invasion, HER2, size of the tumor, in situ component, lymph node involvement ratio, progesterone receptor status, and the total number of dissected lymph nodes. The best model obtained overall accuracy, specificity and sensitivity of 72.49%, 72.83%, and 71.85%, respectively. Conclusion: The performance of model is quite satisfactory due to the fact that we only used features, which could be obtained at the initial steps of diagnosis. Even though, the effect of patient's age is controversial, we concluded that ageing would decrease the 60-month survivability. Our model indicated that having all type of invasions (i.e. vascular, lymphatic, etc.) would result in poorer chance of survival compared to other features effect. [ABSTRACT FROM AUTHOR]

Importance: Accurately predicting major bleeding events in non-valvular atrial fibrillation (AF) patients on direct oral anticoagulants (DOACs) is crucial for personalized treatment and improving patient outcomes, especially with emerging alternatives like left atrial appendage closure devices. The left atrial appendage closure devices reduce stroke risk comparably but with significantly fewer non-procedural bleeding events., Objective: To evaluate the performance of machine learning (ML) risk models in predicting clinically significant bleeding events requiring hospitalization and hemorrhagic stroke in non-valvular AF patients on DOACs compared to conventional bleeding risk scores (HAS-BLED, ORBIT, and ATRIA) at the index visit to a cardiologist for AF management., Design: Prognostic modeling with retrospective cohort study design using electronic health record (EHR) data, with clinical follow-up at one-, two-, and five-years., Setting: University of Pittsburgh Medical Center (UPMC) system., Participants: 24,468 non-valvular AF patients aged ≥18 years treated with DOACs, excluding those with prior history of significant bleeding, other indications for DOACs, on warfarin or contraindicated to DOACs., Exposures: DOAC therapy for non-valvular AF., Main Outcomes and Measures: The primary endpoint was clinically significant bleeding requiring hospitalization within one year of index visit. The models incorporated demographic, clinical, and laboratory variables available in the EHR at the index visit., Results: Among 24,468 patients, 553 (2.3%) had bleeding events within one year, 829 (3.5%) within two years, and 1,292 (5.8%) within five years of index visit. We evaluated multivariate logistic regression and ML models including random forest, classification trees, k-nearest neighbor, naive Bayes, and extreme gradient boosting (XGBoost) which modestly outperformed HAS-BLED, ATRIA, and ORBIT scores in predicting clinically significant bleeding at 1-year follow-up. The best performing model (random forest) showed area under the curve (AUC-ROC) 0.76 (0.70-0.81), G-Mean score of 0.67, net reclassification index 0.14 compared to 0.57 (0.50-0.63), G-Mean score of 0.57 for HASBLED score, p-value for difference <0.001. The ML models had improved performance compared to conventional risk across time-points of 2-year and 5-years and within the subgroup of hemorrhagic stroke. SHAP analysis identified novel risk factors including measures from body mass index, cholesterol profile, and insurance type beyond those used in conventional risk scores., Conclusions and Relevance: Our findings demonstrate the superior performance of ML models compared to conventional bleeding risk scores and identify novel risk factors highlighting the potential for personalized bleeding risk assessment in AF patients on DOACs.