Using Logistic Regression to Predict Factors Affecting Cancer Patients' Response to Treatment

This graduation project applied binary logistic regression to identify factors associated with cancer patients' response to treatment.

The study used real patient data collected from Dongola Cancer Treatment Center for the year 2024, with a sample of 82 cases. The analysis examined several factors, including age, gender, body mass index (BMI), cancer type, comorbidities, and metastasis.

Statistical analysis was conducted using logistic regression to examine the relationship between these independent variables and patients' treatment response. The results indicated significant effects for gender, cancer type, and metastasis, while BMI and comorbidities did not show statistically significant effects in the studied sample.

The project strengthened my practical skills in statistical analysis, logistic regression, data interpretation, and presenting statistical findings in an organized report.