Improved Health Monitoring Informatics by New Similarity Measures

Improved Health Monitoring Informatics by New Similarity Measures

Vijayalakshmi Kakulapati (Sreenidhi Institute of Science and Technology, India), V.V.S.S.S. Balaram (Sreenidhi Institute of Science and Technology, India) and P. Vijay Krishna (Mahindra École Centrale (MEC), India)
DOI: 10.4018/978-1-5225-5222-2.ch012

Abstract

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.
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1. Introduction

Now- a-day healthcare moving towards huge health issues and maintain records. These records are providing for analysis and providing personalized recommendations to patients. The author’s research belongs to multiple health symptoms of diabetic and thyroid from patients using online profiles based, using their online interaction on healthcare applications specially belongs for patients to actively developed different patterns. Many organizations and researchers are working on different predictive analytics on healthcare systems. The authors approach an efficient method for finding similarity between Patients of diabetic and thyroid and find it comfortable to communicate directly with other patients via social networks. This research will identify different patient’s online behaviors 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 utilizing new similarity measures between patients. The proposing function utilizes online records by considering the similarity between patients and provides recommendation for medicines to the patient. For a large number of patients, the new measure assists the minimization of risk and realistic. The authors have established the framework comparison of performance on the assimilation of bunch utilizing algorithm. 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.

Healthcare systems is a promising area which apprehensions itself with experimental, data processing, intelligence tasks of therapeutic observation, learning and investigate in medical domain inclusive of the technologies to sustain these undertakings. Healthcare systems can be defined as “Integration of the theory and practice of information and data management and use in all characteristics of healthcare deliverance and public health”. A number of health informatics disciplines is allows exploring the implementation, advance applications, techniques and instruments.

Healthcare systems create electronic health record (EHR) (C.Sunil kumar et al 2012) to progressing the processes of:

  • 1.

    Health and patient-care data storage, retrieval and communication of data for medical research.

  • 2.

    Medical knowledge is learning and transmission.

In the last decade healthcare applications have developed quickly. Primary health centres moved towards a consecutive healthcare process support from inaccessible systems. Various systems include health practitioners, societies; utilize the technological advances in computing healthcare environments. There is a need for information sharing amongst healthcare systems in interactive systems. The relevant patient medical information is required to available easily for healthcare professionals whenever they need to analyze the information which improves the health care quality. The specific situation is characterized by:

  • 1.

    Healthcare applications can be support by hardware and software solutions in heterogeneous systems also.

  • 2.

    Consumption of EHR is suitable for various healthcare applications in the Heterogeneities structure.

The typical problem in healthcare applications is heterogeneities. Every EHR is unique, which means that the framework of EHR and the processes utilized for interchanging their content may diverge significantly. This interchanging becomes a complication for distribution health information between hospitals.

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