Insights to the Issues, Research Trends and Advancements in Predictive Analysis on comorbid diseases
Keywords:
Comorbidity, Prediction, Machine learning, Predictive modeling, Systematic reviewAbstract
Many significant algorithms, strategies, and frameworks have been created since the implementation of prediction analyses was first presented many years ago in order to enhance performance. Through the study of recent and past medical data, predictive analytics enables medical personnel to identify potential for improving clinical and operational decision-making, forecasting trends, and even controlling the spread of illness. In order to find patterns, correlations, and linkages in the healthcare area, a vast quantity of data must be gathered and analysed. which are employed to strengthen healthcare decision-making, optimise resource allocation, and forecast and improve patient outcomes.The severity of the issues related to prediction analyses such data types have received less attention in the past, yet they nevertheless fall outside of a particular primary study focus. Here will provide a quick overview of current research trends, highlight some significant recent research accomplishments, and explore some significant outstanding questions surrounding prediction analyses of comorbid diseases. We anticipate that this paper will provide a status update with an overview of the success of the research methods used to prediction analyses of comorbid diseases to help forthcoming researchers identify and set up their work in an ideal way for taking into account study gaps.
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