Learn to Build Basic Machine Learning Models from Health Data: A Hands-On Guide for Non-Coders.
- 1Department of PhysiologyAll India Institute of Medical SciencesDeogharJharkhandIndia.
- 2Department of RadiodiagnosisAll India Institute of Medical SciencesDeogharJharkhandIndia.
- 3Department of PhysiologyRaiganj Government Medical College and HospitalRaiganjWest BengalIndia.
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.
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BibTeX
@article{mondal2026learn,
title = {{Learn to Build Basic Machine Learning Models from Health Data: A Hands-On Guide for Non-Coders.}},
author = {Himel Mondal and Pradosh Kumar Sarangi and Shaikat Mondal},
journal = {The Indian journal of radiology \& imaging},
year = {2026},
month = aug,
doi = {10.1055/s-0046-1827802},
url = {https://europepmc.org/article/MED/42921643},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Learn to Build Basic Machine Learning Models from Health Data: A Hands-On Guide for Non-Coders.
AU - Himel Mondal
AU - Pradosh Kumar Sarangi
AU - Shaikat Mondal
PY - 2026
DA - 2026/08/29
JO - The Indian journal of radiology & imaging
DO - 10.1055/s-0046-1827802
UR - https://europepmc.org/article/MED/42921643
AB - 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.
SN - 0971-3026
SN - 1998-3808
ER -