METHODICAL EVALUATION OF HEALTHCARE INTELLIGENCE FOR HUMAN LIFE DISEASE DETECTION

Abstract

Event intelligence for early diseases detection is highly demanded in current era and it requires reliable technology-oriented applications. Trusted emerging technologies play a vital role in modern healthcare systems for early diagnoses of different medical conditions because it helps to speed up the treatment process. Despite the enhancement of current healthcare systems, robust diagnosis of different type of diseases for intra-patients (outside of hospital settings) is still considered as a difficult task. However, the continuous evolution of trusted technologies in health sectors narrate the reboot process which could upgrades the healthcare service provision as the trusted next generation health units. In order to assist the healthcare providers to carry out early diseases’ detection for intra-patient clients, we designed this systematic review. We extracted 40 studies from the databases i.e. IEEE Xplore, Springer, Science direct and Scopus, from March 2016 and February 2021, and we formulated our research questions based on these studies. Subsequently, we rectified these studies using two filtration schemes namely, inclusion-omission policy and quality assessment, and as a result, we obtained 19 studies which successfully mapped our defined research questions .We found that these 19 studies clearly highlighted the different trusted architecture of internet of things, mobile cloud computing and machine learning, that are significantly beneficial to diagnose medical conditions for the intra-patient clients such as neurological diseases, cardiac malfunctions and other common diseases.

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