IJRCS – Volume 5 Issue 1 Paper 4


Author’s Name : K Hemalatha | S K B Rathika

Volume 05 Issue 01  Year 2018  ISSN No:  2349-3828  Page no: 12-17



The general medical examination is a typical type of preventive medication including visits to a general expert by well feeling adults on a regular basis. Making out the ones taking part at risk is important for early suggestions and precautions coming between groups. The big challenge of learning the design for risk of unhealthy life in future lies in the unlabeled data which is a very integral part of the data set which consist of the person’s data who is perfectly healthy and whose condition varies from healthy to ill. In this paper, they propose a graph-based, semi-supervised learning algorithm called SHG-Health (Semi-supervised Heterogeneous Graph on Health) for risk predictions of what will take place in the future to put in order a by degrees undergoing growth place, position with the greater number or part of the facts without mark, name. Here, they will focus mainly on unlabeled data so that system will work for both undiagnosed patient and the healthy one. With this system, people will be getting intimate precaution before even dealing with a disease. Hence, this system will lead to a healthy life.


  1. Adrian Tang, Simha Sethumadhavan, and Salvatore Stolfo(2009),‘Unsupervised Anomaly-based Malware Detection using Hardware Features’.
  2. Mamoun Alazab, Sitalakshmi Venkatraman, Paul Watters and Moutaz Alazab (2011), ‘Zero-day Malware Detection based on Supervised Learning Algorithms of API call Signatures’.
  3. Marco Cova, Christopher Kruegel, and Giovanni Vigna (2012), ‘Detection and Analysis of Drive-by Download Attacks and Malicious JavaScript Code’.
  4. Mohammad Sazzadul Hoque, Md. Abdul Mukit and Md. Abu Naser Bikas, (2000), ‘An Implementation Of Intrusion Detection System Using Genetic Algorithm’.
  5. Robert Moskovitch, Yuval Elovici, and Lior Rokach, (2008), ‘Detection of unknown computer worms based on behavioral classification of the host’, journal of Computers and Security, Vol. 52, pp. 4544–4566.
  6. Shi-Jinn Horng , Pingzhi Fan, Yao-Ping Chou, Yen-Cheng Chang and Yi Pan, (2008), ‘A feasible intrusion detector for recognizing IIS attacks based on neural networks’, journal of Computers and Security, Vol. 27, pp.84-100