[1]. Mohammed Abdul Khaleel has given paper in the Survey of Techniques for mining of data on Medical Data for Finding Frequent Diseases locally. This paper focus on dissect information mining procedures which are required for medicinal information mining particularly to find locally visit illnesses, for example, heart infirmities, lung malignancy, bosom disease et cetera. Information mining is the way toward extricating information for finding inactive examples which Vembandasamy et al. performed a work, to analyze and detect heart disease. In this the algorithm used was Naive Bayes algorithm. In Naïve Bayes algorithm they used Bayes theorem. Hence Naive Bayes has a very power to make assumption independently. The used data-set is obtained from a diabetic research institutes of Chennai, Tamilnadu which is leading institute. There are more than 500 patients in the dataset. The tool used is Weka and classification is executed by using 70% of Percentage Split. The accuracy offered by Naive Bayes is 86.419%.
[2]. Costas Sideris, Nabil Alshurafa, Haik Kalantarian and Mohammad Pourhomayoun have given a paper named Remote Health Monitoring Outcome Success prediction using First Month and Baseline Intervention Data. RHS systems are effective in saving costs and reducing illness. In this paper, they portray an upgraded RHM framework, Wanda- CVD that is cell phone based and intended to give remote instructing and social help to members. CVD counteractive action measures are perceived as a basic focus by social insurance associations around the world.