Heart disease is a leading cause of death worldwide, and the need for effective predictive systems is a major source of the need to treat affected patients. This study aimed to determine how to improve the accuracy of Random Forest in predicting and classifying heart disease. The experiments performed in this study were designed to select the most optimal parameters using an RF optimization technique using GA. The Genetic Algorithm (GA) is used to optimize RF parameters to predict and classify heart disease. Optimization of the Random Forest parameter using a genetic algorithm is carried out by using the Random Forest parameter as input for the initial population in the Genetic Algorithm. The Random Forest parameter undergoes a series of processes from the Genetic Algorithm: Selection, Crossover Rate, and Mutation Rate. The chromosome that has survived the evolution of the Genetic Algorithm is the best population or best parameter Random Forest. The best parameters are stored in the hall of fame module in the DEAP library and used for the classification process in Random Forest. The optimized RF parameters are max_depth, max_features, n_estimator, min_sample_leaf, and min_sample_leaf. The experimental process performed in RF uses the default parameters, random search, and grid search. Overall, the accuracy obtained for each experiment is the default parameter 82.5%, random search 82%, and grid search 83%. The RF+GA performance is 85.83%; this result is affected by the GA parameters are generations, population, crossover, and mutation. This shows that the Genetic Algorithm can be used to optimize the parameters of Random Forest.

Background: Obstructive Sleep Apnea Syndrome (OSAS) is a breathing disorder during sleep that can cause stopping breathing and trigger dangerous diseases. The main symptoms that can occur in OSAS patients are loud snoring and excessive daytime sleepiness so it can disrupt the quality of life and performance. Allergic rhinitis (AR) is one of the risk factors for OSAS, after age, obesity, sex, neck circumference, and anatomic abnormalities of the airway. Allergic rhinitis can disrupt the quality of sleep patients. Allergic rhinitis patient has a risk of OSAS due to obstruction of the upper airways so airflow to the lungs is obstructed.Objective: To analyze the relationship of allergic rhinitis with the incidence of Obstructive Sleep Apnea Syndrome (OSAS) in young adults.Methods: This study is an observational study with a cross-sectional design. Samples were obtained with a probability sampling method by consecutive sampling. The subject of this study were students of the Faculty of Medicine, Diponegoro University, aged 18-23 years. This study consisted of interviews, BMI examination, neck circumference examination, nose, and throat examination, fill the Epworth Sleepiness Scale (ESS) questionnaire to assess OSAS and the Score For Allergic Rhinitis (SFAR) questionnaire to assess allergic rhinitis. Statistical tests use chi-square, fisher's exact test, and logistic regression.Results: The incidence of allergic rhinitis with OSAS in young adults occurred as much as 79,7%. Bivariate analyze showed allergic rhinitis associated significantly with the incidence of OSAS in young adults (p=0,000; PR=12,3). The most common group of allergic rhinitis symptoms in allergic rhinitis patients with OSAS is the group of symptoms of sneezing, rhinorrhea, and nasal congestion.Conclusion: Allergic rhinitis patients risk 12,3 times more likely to suffer from OSAS than non-allergic rhinitis in young adults.Keyword: Allergic Rhinitis, OSAS, Young Adults