SURVIVAL ANALYSIS IN MEDICAL DATASETS

• Main Author: Goh, Joyce Chui Wen
• Format: Final Year Project Report
• Language: English; English
• Published: Universiti Malaysia Sarawak, (UNIMAS) 2020
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://ir.unimas.my/id/eprint/34180/1/Joyce%20Goh%20Chui%20Wen%20-%2024%20pgs.pdf; http://ir.unimas.my/id/eprint/34180/4/Joyce%20GCW.pdf; http://ir.unimas.my/id/eprint/34180/

This project is studied about the survival analysis in several medical datasets. Survival analysis is a method for data analysis in which the outcomes indicate the time to the occurrence of an event of interest. By time, it can be years, month, weeks or days from the beginning of follow-up of an individual until an event occurs; alternatively, it can refer to the age of an individual when an event occurs. In medical studies, time to death is the event of interest. This study is based on 11627 observations comprising of 6605 females and 5022 males. The age of the observations is in the range of 32-81 years old. The dataset is retrieved from Framingham Heart Study. The data was collected during three examination periods, approximately 6 years apart. The aim of this research is to explore the selected medical datasets by using data visualization techniques for better insight and manipulation tools in R Studio. The Kaplan-Meier plot was used to study the general pattern of survival which showed the survival rate of the patients. Cox regression was used to study the regression coefficient, hazard ratio, standard error, statistical significance, p, Likelihood ratio test, and p-value. The result shows that gender, age, and blood pressure are found impacting the disease development.