The study aims to describe the sociodemographic and lifestyle characteristics of breast cancer patients in Meru, examine the distribution of intrinsic breast cancer subtypes, and identify the clinical and demographic factors associated with these subtypes. In addition, the...
This project involved developing a machine learning pipeline to predict patient risk levels using structured clinical data. The workflow covered data cleaning, exploratory analysis, feature preparation, model training, and risk segmentation to identify patterns associated with...
This project focused on disease outcome survival analysis using Kaplan–Meier survival curves and Cox proportional hazards models to evaluate how clinical factors influence patient survival over time. The analysis included proportional hazards assumption checks, survival...
The project analyzes maternal health data patterns to identify the most correlated factors to health risks during pregnancy. It uses regression analysis to identify conditions or characteristics that increase levels of risk, with the aim of facilitating earlier intervention,...
