Learn to Build Basic Machine Learning Models from Health Data: A Hands-On Guide for Non-Coders

Abstract

The increasing integration of artificial intelligence in health care has created new opportunities for improving diagnosis, prognosis, and clinical decision-making. However, the adoption of machine learning (ML) by health care professionals remains limited due to the technical complexity associated with coding and data analysis. This review article aims to bridge this gap by providing a practical, hands-on guide for non-coders to build basic ML models using structured health data. We describe a simplified four-step workflow consisting of data cleaning, model building and evaluation, external validation, and deployment for clinical use. To operationalize this process, we introduce four user-friendly, no-code applications developed using R and Shiny—CleanSight, MLSight, ValidateSight, and PredictSight. These tools enable users to preprocess data, train and evaluate ML models, and generate real-time predictions through intuitive graphical interfaces without requiring programming skills. This workflow is designed for structured/tabular clinical data and does not include computer vision tasks such as image classification or segmentation. A case study using a fictitious dataset on nonalcoholic fatty liver disease is presented to demonstrate the complete workflow, including handling missing data, training a prediction model, and applying it in a simulated clinical scenario. The applications are designed to run on standard personal computers, making them accessible in routine health care and academic settings. By simplifying complex ML processes and emphasizing practical usability, this guide empowers clinicians, including radiologists, to transform routine clinical data into actionable predictive tools. The approach has the potential to enhance data-driven clinical practice and promote wider adoption of ML in health care, particularly among users with limited technical expertise.