Coimbra BreastCancer

Coimbra Breast Cancer Study

This dataset presents a comprehensive exploration of clinical features observed or measured for 64 patients with breast cancer and 52 healthy controls. The dataset encompasses both quantitative attributes and corresponding labels for effective analysis and modeling.

Quantitative Attributes:

• Age (years): Age of the individuals.
• BMI (kg/m²): Body Mass Index, a measure of body fat based on weight and height.
• Glucose (mg/dL): Blood glucose levels, an important metabolic indicator.
• Insulin (µU/mL): Insulin levels, a hormone related to glucose regulation.
• HOMA: Homeostatic Model Assessment, a method for assessing insulin resistance and beta-cell function.
• Leptin (ng/mL): Leptin levels, a hormone involved in regulating appetite and energy balance.
• Adiponectin (µg/mL): Adiponectin levels, a protein associated with metabolic regulation.
• Resistin (ng/mL): Resistin levels, a protein implicated in insulin resistance.
• MCP-1 (pg/dL): Monocyte Chemoattractant Protein-1, a cytokine involved in inflammation.

Labels:

1: Healthy controls
2: Patients with breast cancer

This dataset serves as a valuable resource for researchers and healthcare professionals aiming to analyze the intricate relationship between clinical attributes and breast cancer. The inclusion of quantitative measures provides a detailed perspective, enabling the development of predictive models and the identification of potential biomarkers associated with breast cancer.

Researchers can leverage this dataset to explore patterns, correlations, and insights that contribute to a better understanding of the factors influencing breast cancer. The clear labeling facilitates supervised learning tasks, making it a versatile dataset for both exploratory analysis and the development of machine learning models in the domain of breast cancer research.