Similarities and Differences between Machine Learning and Traditional Advanced Statistical Modeling in Healthcare Analytics
This work addresses the debate among data scientists and statisticians in healthcare analytics, but it is incremental as it synthesizes existing knowledge without introducing new methods or data.
The paper examines the relationship between machine learning and traditional statistical modeling in healthcare analytics, concluding that they are complementary approaches based on similar mathematical principles, with the choice depending on problem-specific factors like data characteristics and desired outcomes.
Data scientists and statisticians are often at odds when determining the best approach, machine learning or statistical modeling, to solve an analytics challenge. However, machine learning and statistical modeling are more cousins than adversaries on different sides of an analysis battleground. Choosing between the two approaches or in some cases using both is based on the problem to be solved and outcomes required as well as the data available for use and circumstances of the analysis. Machine learning and statistical modeling are complementary, based on similar mathematical principles, but simply using different tools in an overall analytics knowledge base. Determining the predominant approach should be based on the problem to be solved as well as empirical evidence, such as size and completeness of the data, number of variables, assumptions or lack thereof, and expected outcomes such as predictions or causality. Good analysts and data scientists should be well versed in both techniques and their proper application, thereby using the right tool for the right project to achieve the desired results.