Diabetes and hypertension have become very common perhaps because of increasingly busy lifestyles, unhealthy eating habits, and a highly competitive workplace. The rapid advancement of mobile communication technologies offers innumerable opportunities for the development of software and hardware applications for remote monitoring of such chronic diseases. This study describes a remote health-monitoring service that provides an end-to-end solution, that is, (1) it collects blood pressure readings from the patient through a mobile phone; (2) it provides these data to doctors through a Web interface; and (3) it enables doctors to manage the chronic condition by providing feedback to the patients remotely. This article also aims at understanding the requirements and expectations of doctors and hospitals from such a remote health-monitoring service.

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Outliers are eccentric data points with anomalous nature. Clustering with outliers has received a lot of attention in the data processing community. But, they inordinately affect the quality of the results obtained in case of popular clustering algorithms during the process of finding an optimal solution. In this work, we propose a novel method to classify the data points with grouping characteristics as either an outlier or not. We use both distance and density of a particular data point with respect to the rest of the data points for this process. Distances are used to find the points at the extremities while the densities are used to identify the data points at the sparsest spaces. Further, every data model has to take into account the aspect of generalization in order to work robustly even in out of the box situations. Hence, our approach provides a generalization aspect to the model. The accuracy of the proposed work is measured using area under curve (AUC) was found the highest for cardioto data set -AUC value-0.90 and second highest AUC value was obtained for Spambase data set -0.52 and several other datasets are used to demonstrate the usage of the model proposed.

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