Huge amount of health care data are available online to improve the overall performance of health care system. Since this huge health care Big-data is valuable and sensitive, it requires safety. In this paper we analyze numerous ways in which the health care Big-data can be protected. In recent days many augmented security algorithm that are suitable for Big-data have emerged like, El-Gamal, Triple-DES, and Homomorphic algorithms. Also authentication and access control can be implemented over Big-data using Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) schemes.Along with security to Big-data we try to evolve the ways in which the valuable Big-data can be optimized to improve the Big-data analysis. Mathematical optimization techniques such as simple and multi-purpose optimization and simulation are employed in Big-data to maximize the patient satisfaction and usage of doctor’s consulting facility. And also, to minimize the cost spent by patient and energy wasted.
In developing countries pediatric pneumonia is the second leading cause of deaths and 98% of pneumonia-induced deaths are identified across the world. It is mandatory to identify the symptoms of pneumonia in children to avoid mortality causing complications. Early identification of children at risk for treatment failure or at increased risk for death will help to improve overall health outcomes. If pneumonia is suspected, it is important to seek medical attention promptly so that an accurate diagnosis can be made and appropriate treatment is given in time. The proposed approach quickly provides history of previous patient's details, expert doctor's opinions who are in globe and their previous treatment for the same symptoms, all diagnostic reports such as blood tests, x-ray etc., from the cloud and gives analytics from big data to take fast and precise decisions by the doctors.
In modern era of medical field, kidney diseases are the foremost common diseases found in the majority of the populations in subsequent years. The essential is diagnosing the problem and however advanced task that ought to be accomplished precisely with proficiently. In most of the cases, kidney condition leads to loss of life. Diagnosing could be a tough thus the intention of the research is to develop a neural-trained system to spot kidney condition risk of the patients. The Artificial Neural network will collect the data from the previous method and it will be helpful to identify the chance factors on the premise of provided data. This paper uses neural techniques for diagnosing of the kidney disease. MATLAB simulation was employed as a development tool.
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