Abstract
The developing information in the medical community has made the adoption of big data methods to improve the quality of healthcare. Existing machine learning tools will help to overcome this bottleneck problem. With the greatness of technology, AI-automated machine learning has come into existence and it is used to deploy the machine learning models for further use. The data of diseases, patients’ sufferings; their treatment, etc., are being stored and managed accordingly. Again security is an important issue for HIV-like diseases so here cloud computing techniques will be implemented for continuous monitoring of data. All the health records are maintained in databases in electronic form. Hospital researches and all diagnoses are also on continuous monitoring which disables data breaching. Here a surveillance machine learning healthcare model (SMHC) is developed. This model will be efficient, effective, and properly managed big electronic health data. This chapter discusses these intelligent and sustainable approaches to managing electronic health data more securely. The main contribution of this chapter is to develop an SMHC model to enhance security in the healthcare system by bringing real-time alerts and delivering authentic predictions to medical seekers.
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