Methods for Inclusive Underwriting of Breast Cancer Risk with Machine Learning and Innovative Algorithms

INTRODUCTION

-Due to early detection and improved therapies, the prevalence of long-term breast cancer survivors is increasing. This has increased the need for more inclusive underwriting in individuals with a history of breast cancer. Herein, we developed a method using algorithm aiming facilitating the underwriting of multiple parameters in breast cancer survivors.

METHODS

-Variables and data were extracted from the SEER database and analyzed using 4 different machine learning based algorithms (Logistic Regression, GA2M, Random Forest, and XGBoost) that were compared with Kaplan Meier survival estimates. The performances of these algorithms have been compared with multiple metrics (Log Loss, AUC, and SMR). In situ (non-invasive) and metastatic breast cancer were excluded from this analysis.

RESULTS

-Parameters included the pathological subtype, pTNM staging (T: tumor size, N; number of nodes; M presence or absence of metastases), Scarff-Bloom-Richardson grading, the expression of estrogen and progesterone hormone receptors were selected to predict the individual outcome at any time point from diagnosis. While all models had identical performance in terms of statistical metrics (AUC, Log Loss, and SMR), the logistic regression was the one and only model that respects all business constraints and was intelligible for medical and underwriting users.

CONCLUSION

-This study provides insight to develop algorithms to set underwriter-friendly calculators for more accurate risk estimations that can be used to rationalize insurance pricing for breast cancer survivors. This study supports the development of a more inclusive underwriting based on models that can encompass the heterogeneity of several malignancies such as breast cancer.

Partial migration of a maraena whitefish Coregonus maraena population from the River Elbe, Germany

The maraena whitefish Coregonus maraena is a threatened anadromous species in the North Sea, which in the past was decimated to near extinction. Since the late 1980s, several re-establishment programs have been implemented in rivers draining into the North Sea, but the scientific basis for sustainable conservation measures is often lacking, since little is known about the biology of this species. In this study, otolith microchemistry of fish ranging from 24.6 to 58.4 cm in total length (median 31.3 cm, SD 8.4 cm) was used to characterize the migration behavior of a reintroduced population of maraena whitefish from the River Elbe, Germany. Our analyses revealed the presence of 3 different migration patterns: (1) one-time migration into high-salinity habitat (North Sea) within the first year of life (29.6%), (2) multiple migrations between low- and high-salinity habitats starting in the first year of life (14.8%) and (3) permanent residency within low-salinity habitats, a pattern displayed by the majority (55.6%) of sampled individuals. Not only do these results reveal differential migration behavior, but they also indicate that permanent river residency is common in the River Elbe population of C. maraena. The role of the Elbe as both a feeding and a spawning habitat should thus be considered more explicitly in current conservation measures to support recovery of this species.