PREDIKSI DAN ANALISIS FAKTOR GAYA HIDUP TERHADAP SKOR KESEHATAN MENGGUNAKAN REGRESI LINEAR DI RAPIDMINER

Abstract

Health is an important asset in human life that is greatly influenced by daily lifestyle. This study aims to predict and analyze individual health scores based on lifestyle factors such as age, body mass index (BMI), exercise frequency, diet quality, sleep duration, smoking status, and alcohol consumption. The method used is linear regression with the help of RapidMiner software as an analysis tool. The test results show that all variables have a statistically significant effect on health scores (p <0.05). The best model is obtained when the proportion of training data is 90% and the testing data is 10%, with the lowest RMSE value of 5,495. Regression analysis shows that sleep duration, exercise frequency, and diet quality have a positive effect on health scores, while age, BMI, smoking habits, and alcohol consumption have a negative impact. These findings support the importance of a healthy lifestyle in improving health quality, and prove that linear regression can be an effective approach in data-based decision support systems in the health sector.