Comparative Study on Healthcare Cost Prediction Using Deep Learning Data Analytics

As an impact of the data silos from sophisticated methods and the density of healthcare systems, it is essential toanalyze accurate forecasting. Healthcare expenses and policy-making have become more effective as the globalexpenditure on healthcare costs increases, which can be predicted and managed. This article examines thecomparative effectiveness of classical regression techniques and machine learning (ML) and deep learning (DL)algorithms to predict the costs involved in the healthcare business. Numerous studies have shown the superiorpredictive performance of ML models in healthcare cost forecasting compared to classical regression methods. Thecomplexity of healthcare costs can be analyzed with higher precision and accuracy by ML algorithms. However, theweakness is in their intelligibility; ML models are definitely referred to as "black boxes," which can hinder theiradoption in data analytics that requires transparency, neutrality, and an interpretability of the decision-making results.Widespread resources and proficiency in data science are needful for the implementation of ML approaches, whichprovide innovative capabilities. More importantly, the quality of forecasts is contingent upon the structure and patternof the data source. On the other hand, regression methods are simple to manipulate; nevertheless, they may not reflectelaborate trends. In the prediction, the use of ML, DL, and classical regression methods is influenced by the dataset'sconstraints. For instances where interpretability and data source constraints are critical, classical regression may beappropriate. Nevertheless, machine learning approaches are regarded as essential tools for attractive accuracy andthe further development of complex data models.