Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models - Chapter 4 - dataset - Machine Learning Models Applied to Biomedical Engineering

Published: 6 June 2022| Version 1 | DOI: 10.17632/gnmbrzkshp.1
Contributor:
Jorge Garza-Ulloa

Description

“Machine Learning (ML)” is a subset of “AI” that evolved from the study of “pattern recognition and computational learning theory.” “ML” has seven specific steps to follow to achieve its goal of obtaining a valid model for prediction. The “ML Model” to select to find the solution depends on the type of ML problem; generally, this can be “unsupervised learning,” “supervised learning,” “reinforcement learning,” “survival models,” “association rules,” and others. Please read this chapter at science direct: https://www.sciencedirect.com/science/article/pii/B9780128207185000027 or buy the book or eBook at https://www.elsevier.com/books/applied-biomedical-engineering-using-artificial-intelligence-and-cognitive-models/garza-ulloa/978-0-12-820718-5. This data set is organized in 3 folders: Matlab_ML (ML using Matlab), SPSS_MODELER (ML using IBM Watson SPSS Modeler) and Watson_ML (ML using IBM Machine Learning Models) Section 4.12.2.1 Research 4.1 Tutorial IBM Watson SPSS Modeler Flow for “ML Model for Diabetes” (Folder SPSS_MODELER > SPSS_Clusters) Section 4.12.2.2 Research tutorial 4.2 IBM Watson SPSS Modeler Flow for “Heart disease ML model and deployment” (folder SPSS_MODELER > SPSS_Classifier) Section 4.12.2.3 Research tutorial 4.3 IBM Watson SPSS Modeler Flow for “Kidney disease ML Auto Classifiers Models and deploy the best model” (folder SPSS_MODELER > SPSS_AutoClassifier) Section 4.12.2.4 Research tutorial 4.4 IBM Watson AutoAI experimenter for “Breast cancer ML model and deploy the best model” (folder Watson_ML) Section 4.12.2.5 Research tutorial 4.5 MATLAB: Statistics and Machine Learning Toolbox for a “Diabetes dataset AI modeling for Classifier Model and a Regression Model” (folder Matlab_ML)

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Institutions

University of Texas at El Paso

Categories

Medicine, Biomedical Engineering, Machine Learning

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