Prediction of Ovarian Cyst Disease Mortality Rate Cases Using Markov Chain Monte Carlo with Gibbs Sampling Algorithm
DOI:
https://doi.org/10.47709/cnahpc.v7i3.6724Keywords:
Bayesian method, markov, Monte Carlo MetodeAbstract
Ovarian cyst is one of the reproductive disorders that can develop into ovarian cancer and cause death if not treated properly. This study aims to predict the death rate due to ovarian cyst disease using the Markov Chain Monte Carlo (MCMC) method with the Gibbs Sampling algorithm. The data used is secondary data from Malahayati Islamic Hospital Medan City in 2024, which consists of 15 patients, including one deceased patient (fictitious) for the purposes of the classification model. The independent variables used include age, length of hospitalization, and number of diagnoses, while the dependent variable is the patient's death status. The estimation process was conducted with 600 iterations, where the initial 100 iterations were used as burn-in, and the rest were used to obtain the posterior mean of the model parameters. The results show that the model is able to predict death status with 100% accuracy, where all predictions match the actual data. The parameter coefficients show that the higher the age, the longer the hospitalization, and the more the number of diagnoses, the higher the risk of death. The MCMC method with Gibbs Sampling algorithm proved to be effective in generating probabilistic predictions as well as identifying important factors that affect the risk of death of patients with ovarian cysts
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