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Building a Binary Classification Machine-Learning Model: A Guide to Predicting Participation in a Lyme Disease
Kunal Garg1, Liria Mitzuko Fajardo-Yamamoto2, Flor Cecilia Rojas-Castro2
1Tezted Ltd, Jyväskylä, Finland. kunal.garg@tezted.com.
Methods in Molecular Biology (Clifton, N.J.)
|January 2, 2024
Summary
This study introduces a framework for building Random Forest classification models. The model predicts Lyme disease program participation using patient data, aiding researchers in machine learning applications.
Area of Science:
- Data Science
- Machine Learning
- Medical Informatics
Background:
- The expanding fields of data analysis and machine learning offer many resources.
- However, a comprehensive framework for building machine learning models is lacking for researchers.
Purpose of the Study:
- To present a step-by-step framework for constructing a robust Random Forest classification model.
- To demonstrate the model's application in predicting Lyme disease program participation using patient data.
Main Methods:
- Developed a structured framework for Random Forest classification model creation.
- Utilized patient data including age, symptoms, blood count, and chemistry results for model training and prediction.
Main Results:
- Successfully trained a Random Forest classification model.
- The model predicts participation in the Lyme disease program based on provided patient data.
Conclusions:
- The presented framework serves as a valuable starting point for researchers in machine learning model development.
- Encourages further exploration and innovation in data analysis and model creation techniques.
