Big Data Analytics for Early Detection and Prevention of Age-Related Diseases in Elderly Healthcare

Abstract

The exponential growth of the elderly population poses considerable obstacles to healthcare systems on a global scale, hence requiring the implementation of inventive strategies to identify and mitigate age-related illnesses at an early stage. The primary objective of this study is to explore the use of big data analytics to improve healthcare practices. Specifically, the emphasis is on identifying possible risk factors and developing proactive treatments for senior citizens. The research technique used in this study is based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) declaration of 2020. This approach is utilised to ensure a thorough and transparent review of the relevant literature. Moreover, the use of Rstudio software is prevalent in the field of data processing, statistical analysis, and visualisation. By conducting a comprehensive examination of academic databases and medical literature, this study undertakes an analysis of a collection of pertinent papers to explore the significance of big data analytics in the early diagnosis and prevention of diseases in senior populations. The studies that have been chosen include a wide range of healthcare fields, such as cardiology, neurology, cancer, and geriatrics. This selection aims to provide a thorough comprehension of existing practises and identify any possible areas that may need more attention. The results of this study emphasise the significant impact that big data analytics may have on healthcare for the elderly. Using extensive and varied datasets, sophisticated analytical methodologies such as machine learning algorithms and data mining allow the detection of nuanced patterns and correlations that might function as precursors for age-related ailments.