Classification and predictive models using supervised machine learning: A conceptual review.

Area of Science:

• Medical machine learning
• Clinical decision support

Background:

• Supervised machine learning models (SMLMs) are increasingly prevalent in medical research.
• These models hold significant potential for enhancing clinical prediction and classification.
• Understanding SMLMs is crucial for advancing medical AI applications.

Purpose of the Study:

• To provide a comprehensive overview of Supervised Machine Learning Models (SMLMs) for medical applications.
• To guide the interpretation of SMLMs within the medical literature.
• To illustrate key concepts with practical clinical examples.

Main Methods:

• Conceptual review of Supervised Machine Learning Models (SMLMs).
• Discussion of core machine learning concepts relevant to healthcare.
• Explanation of model development, validation, and interpretability.

Main Results:

• SMLMs can significantly improve clinical decision-making.
• A structured approach to understanding SMLMs aids their effective application.
• Clinical examples demonstrate the practical utility of SMLMs.

Conclusions:

• Supervised machine learning models are vital tools in modern medical research.
• This review serves as a foundational guide for researchers and clinicians.
• Effective interpretation and application of SMLMs can enhance patient care.