<div><div><p>Early risk prediction and appropriate treatment are believed to be able to delay the occurrence of hypertension and attendant conditions. Many hypertension prediction models have been developed across the world, but they cannot be generalized directly to all populations, including for Indonesian population. This study aimed to develop and validate a hypertension risk-prediction model using machine learning (ML). The modifiable risk factors are used as the predictor, while the target variable on the algorithm is hypertension status. This study compared several machine-learning algorithms such as decision tree, random forest, gradient boosting, and logistic regression to develop a hypertension prediction model. Several parameters, including the area under the receiver operator characteristic curve (AUC), classification accuracy (CA), F1 score, precision, and recall were used to evaluate the models. Most of the predictors used in this study were significantly correlated with hypertension. Logistic regression algorithm showed better parameter values, with AUC 0.829, CA 89.6%, recall 0.896, precision 0.878, and F1 score 0.877. ML offers the ability to develop a quick prediction model for hypertension screening using non-invasive factors. From this study, we estimate that 89.6% of people with elevated blood pressure obtained on home blood pressure measurement will show clinical hypertension.</p></div></div>

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Adolescences' excessive online self-disclosure is now a social phenomenon arising in social media use. The adolescences also tend to share their privacy. This study aims to determine whether extraversion personality, perceived privacy risks, the convenience of maintaining relationships, and online self-presentation influence self-disclosure in adolescents. This study involved 619 adolescents (185 male and 434 female) aged 13-22 years (M = 19.39, SD = 1.83). The participants are active social media users collected from several areas in Indonesia. Multiple regression analysis is used to test the hypothesis. The results show that several variables simultaneously affect online self-disclosure in adolescents (R2 = .422; F (4, 614) = 111.944, p < .01). However, in details, online self-presentation does not have a significant effect on online self-disclosure among adolescents. This result shows that personality factors and adolescent perceptions of the low privacy risk on social media, as well as the goal of maintaining social relations with other members of social media, encourage them to be more online disclose on social media.