Exploring heart diseases survival patterns: A multifaceted approach using survival analysis techniques on time to event data

Heart disease is a serious health issue, and effective management requires an awareness of the factors determining survival.This work used Kaplan-Meier analysis and Cox proportional hazard and frailty survival models on heart disease data.With p-values less than 0.05, our results show that factors including "Age", "Anaemia", "Creatinine Phosphokinase", "Ejection Fraction", "High Blood Pressure", "Serum Creatinine", and "Serum Sodium" are significant predictors of survival.The Cox proportional model, showed these factors effects, and their importance was highlighted by the frailty survival model using the AIC and BIC metrics.A high initial survival probability of 99.7% was revealed by Kaplan-Meier analysis, which subsequently dropped to 57.6%.With a median survival time of 111.5time units, the mean survival time was calculated to be roughly 125.19time units.These findings support risk assessment and patient treatment strategies by offering important new information on heart disease survival patterns and risk variables.