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This research will identify different patient's online behaviours and similarities that can help patient-clinician communications to improve to discover the future or additional threats to raise awareness of causes and consequences. To scale the model, the prototype will rely on the High Performance Computing (HPC) platform running Hadoop file system for storing patient data at distributed locations and Map-reduce paradigm with machine learning algorithms will be deployed to detect the symptoms. In this approach the authors protect online data of patients from privacy issues. In this, the author's effort this difficulty by means of a new advance utilising new similarity measures between patients. The authors are also providing a research investigation on grouping behavior which is affecting by diverse series demonstration, diverse distance similarity measures, the number of genuine patients, and the number of online doctors obtainable, similarity among patient symptoms, minimizing the feasibility, the number of patients for sittings, and the number of clusters to form.