Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment

Area of Science:

• Medical Informatics
• Computational Biology
• Biostatistics

Background:

• Machine learning (ML) and traditional statistical methods are increasingly applied in healthcare.
• ML focuses on predictive accuracy, while traditional methods emphasize inferential relationships.
• Existing ML applications in medicine often lack a structured, comparative framework.

Purpose of the Study:

• To compare the utility and limitations of traditional statistical methods versus ML in medical applications.
• To evaluate performance on patient outcomes like survival, treatment response, and patient-reported outcomes (PROs).

Main Methods:

• Comparative analysis of traditional statistical approaches and machine learning algorithms.
• Evaluation of methods based on data characteristics (e.g., number of variables vs. cases) and domain knowledge.

Main Results:

• Traditional statistical methods are suitable for large datasets with substantial prior knowledge, such as in public health.
• ML shows promise in data-rich, innovative fields like omics, radiodiagnostics, drug development, and personalized medicine.
• Neither approach is universally superior; their effectiveness is context-dependent.

Conclusions:

• The choice between traditional statistics and ML depends on the specific medical application and data landscape.
• Integrating both statistical and ML approaches offers a more robust strategy than relying on a single methodology.
• Future healthcare analytics should leverage the complementary strengths of both traditional and ML techniques.