LRCMP: A Sequential Statistical Framework for Predicting Cancer Mortality Rate

Over 10 Million deaths in the world are because of cancer. Cancer is the second leading cause of death after cardiovascular disease. Additionally, Cancer has significant effects on the socioeconomic status of a family. There are several studies about socioeconomic status and cancer. This work firstly focuses on exploring the relationships between socioeconomic status and cancer mortality rate from disparate open-source data using statistical analysis. Initially, the data consists of 34 features which are reduced to 13 most relevant features using the backward selection method. Secondly, based on the cancer data, we build an appropriate model that can predict the cancer death rate. Specifically, a linear regression model is built and trained for cancer mortality rate prediction. Several models were first built and linear regression diagnostics are performed on the models to check for any assumption violations, finally, the most appropriate model is selected and fine-tuned to provide optimized results. The model is assessed and R2 and RMSE are used to evaluate the model's performance, the model achieved an R2 of 81.12% and RMSE score of 12.23 on test data. Our work also highlights the importance of checking regression assumptions in linear regression modeling.