International Journal of Academic and Applied Research (IJAAR)   Year: 2019 | Volume: 3 | Issue: 10 | Page No.: 5-12 Predicting Blood Pressures and Heart Rate Associated with Stress Level Using Spline Estimator: A Theoretically Discussion Nur Chamidah, Budi Lestari, Toha Saifudin Abstract: Hypertension has become a serious health problem in Indonesia because of its prevalence, however, the causative factors could not be ascertained for about ninety percent of the patients. Various studies have found several risk factors causing hypertension to be obesity, family history, stress levels, heart rate, and an unhealthy lifestyle. In this case, the variables are considered influential on hypertension through a regression function without a specific pattern, i.e., a regression function of multi-response nonparametric regression model. The basic idea of multi-response nonparametric regression where there are correlations between responses is to let the data decide which regression function fits the best without imposing any specific form on it. In this paper we present a theoretically discussion in predicting blood pressures and heart rate affected by stress level that can be used for early detection of hypertension by using spline estimator in multi-response nonparametric regression. The estimated regression function that draws association between blood pressures, heart rate, and stress level can be obtained by taking solution of penalized weighted least square optimization by using reproducing kernel Hilbert space approach. Next, we can get the optimal smoothing parameter by minimizing generalized cross validation function. In this paper we obtain predicted model of blood pressures and heart rate associated with stress level which can be used for early prediction of hypertension.