Predicting Student Blood Pressure by Support Vector Machine Using Facebook

• Published in: 2014 IEEE World Congress on Services pp. 486 - 492
• Main Authors: Khan, Shazada Muhammad Umair, Manzoor, Javeria Shaikh, Lee, Scott UK Jin
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2014
• Subjects: Blood pressure; Error analysis; Facebook; Hanyang University; Kernel; Support Vector Machine; Support vector machines; Training
• ISSN: 2378-3818
• DOI: 10.1109/SERVICES.2014.92

Predicting human blood pressure (B.P) is an important aspect of primary emotion using Facebook has not yet been investigated. Primary emotions help a person to express her/his feelings, thoughts and understanding the importance of social connections using Facebook. Facebook contribute rich environment of primary emotion and famous social site having collection of information that concerned with primary emotions. The well-known machine learning approaches have known as novel methods for doing prediction using SNS. Support Vector Machine (SVM) has recently been a strong machine learning and data mining tool. Our article, it is used to predict human BP. The dataset contain primary emotion and blood pressure that are collected using Facebook post that consists of formal text from come forward of hanyang university student. Current human B.P and those belonging up to six previous primary emotions and B.P values with respect to human emotion are given as input variables, while the blood pressure used as output parameter. The outcome shows that SVM can be prosperously applied for prediction of B.P through primary emotion. On the contrary, validations signify that the error statistics of SVM model marginally outperforms.