Abstract
Predictive analytics is a subfield of advanced analytics that uses historical data along with statistical modelling, data mining, and machine learning to forecast future events. Businesses use predictive analytics to look for trends in this data to pinpoint possibilities and dangers. This meta-analysis explores the landscape of predictive analysis within medical healthcare, examining methodologies, applications, challenges, and future directions. By synthesizing existing literature, this study offers insights into the effectiveness, limitations, and potential advancements in predictive analytics within the healthcare domain. This meta-analysis aims to provide a comprehensive overview of the state of predictive analysis in medical healthcare, highlighting key methodologies, applications, challenges, and future directions. To conduct this meta-analysis, a systematic approach was employed. Inclusion criteria encompassed studies focusing on predictive analysis in medical healthcare published in peer-reviewed journals. Databases such as PubMed, IEEE Xplore, and Scopus were searched using relevant keywords. Data extraction involved identifying key methodologies, applications, and challenges discussed in each study. Quality assessment was performed to ensure the reliability of included studies and minimize bias.
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