Nowadays health is considered as a backbone in terms of performance based on Internet of things (IoT devices), which turned out to be important in diagnosing health level of person with the type of disease a person is suffering with plus its severity level. Basically, IoT sensors operate on medical devices produce large volume of dynamic data. The fluctuation in health data, which forced to use data mining tools and techniques for extracting useful data. Therefore, for applying data mining techniques, heterogeneous data needs to be preprocessed. Therefore, by refining the collection of data, health parametric data mining yields better results with associated benefits. The decision tree is proposed in order to consolidate the health attributes of the students to decide the metrics of health scale. This could lead to evaluate the level of performance of the student in class. After mining the student’s health data it is passed to K-Fold cross validation check, so that to determine the accuracy, error rate, precision and recall. The proposed method is considered as an enhanced diagnosis method with fixed patterns for decision tree to make precise decisions. By considering a case study of student’s health prediction based on certain attributes with its levels, the diagnostic such as pattern based using K-NN and decision tree algorithm are tested on trained dataset using WEKA tool. At the end, the comparison of different algorithms will be reflected to generalize the introduction of optimized classification algorithm.
A qualitative survey on frequent subgraph mining