Data mining and visualization for prediction of multiple diseases in healthcare

A vast amount of data is generated in the fields of healthcare and diagnostics, doctors have to make a direct contact with patients to determine the wounds, injuries and diseases by which the patient is affected. This paper highlights the application of classifying and predicting a specific disease by implementing the operations on medical data generated in the field of medical and healthcare. In this project an efficient multiclass Naïve Bayes algorithm is used for prediction of a particular disease by training it on a set of data before implementation. Wrong clinical decisions taken by medical practitioners can cause any harm or result in serious loss of life of a patient which is hard to afford by any hospital. To acquire a precise and cost effective treatment, technology based Data Mining Systems can be constructed to make worthy decisions. The main aim of this project is to build a basic decision support system which can determine and extract previously unseen patterns, relations and concepts related with multiple disease from a historical database records of specified multiple diseases. The proposed system can solve difficult queries for detecting a particular disease and also can assist medical practitioners to make smart clinical decisions which traditional decision support systems were not able to. The decisions taken by medical practitioners with the help of technology can result in effective and low cost treatments. There is an insufficiency of technology and analysis system and methods to discover connections, concepts and patterns in the medical data. Data mining is an engineering study of extracting previously undiscovered patterns from a selected set of data. In this paper, data mining methods namely, Naive Bayes and J48 algorithms are compared for testing their accuracy and performance on the training medical datasets. The medical datasets will be visualized by different visualization techniques like 2D/3D graphs, pie charts and other methods. The algorithms mentioned above are compared and evaluated on basis of their accuracy and time consumption factors. The algorithm which gives out high accuracy in the comparative study is selected for implementation for developing the system.