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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Published on: January 11, 2020

Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms.

Victor Chang¹, Jozeene Bailey², Qianwen Ariel Xu²

¹Department of Operations and Information Management, Aston Business School, Aston University, Birmingham, UK.

Neural Computing & Applications

|March 29, 2022

Summary

This summary is machine-generated.

This study introduces interpretable machine learning models for diagnosing type 2 diabetes via the Internet of Medical Things. Naïve Bayes and random forest models show promise for accurate e-diagnosis in healthcare.

Keywords:

Diabetes mellitus Interpretable artificial intelligence Machine learning The Internet of Medical Things (IoMT)

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Modeling and Evaluation of Murine Diabetic Cardiomyopathy Model

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Published on: November 29, 2024

Area of Science:

Medical Informatics
Machine Learning in Healthcare
Diabetes Diagnostics

Background:

Machine learning (ML) models are increasingly used in healthcare, but their "black box" nature hinders adoption.
Interpretable ML is crucial for trust and implementation in clinical settings, especially for chronic diseases like diabetes mellitus.
The Internet of Medical Things (IoMT) offers a platform for remote health monitoring and diagnosis.

Purpose of the Study:

To propose and evaluate interpretable supervised machine learning models for the e-diagnosis of type 2 diabetes within an IoMT environment.
To compare the performance of Naïve Bayes, random forest, and J48 decision tree models using key diagnostic metrics.
To assess the decision-making processes of these models to enhance their clinical utility.

Main Methods:

Utilized the Pima Indians diabetes dataset for training and testing machine learning models.
Implemented and compared three interpretable supervised learning algorithms: Naïve Bayes classifier, random forest classifier, and J48 decision tree.
Evaluated model performance based on accuracy, precision, sensitivity, and specificity using the R programming language.

Main Results:

The study analyzed the performance metrics of each machine learning model for diabetes diagnosis.
Naïve Bayes demonstrated strong performance with a carefully selected set of features for binary classification tasks.
Random forest models proved effective when utilizing a larger number of features, indicating adaptability to complex datasets.

Conclusions:

Interpretable machine learning models can be effectively applied for the e-diagnosis of type 2 diabetes in IoMT settings.
Model selection (Naïve Bayes vs. random forest) depends on the feature engineering approach and desired complexity.
Further refinement of feature selection and model interpretability can improve the reliability and acceptance of ML-based diagnostic tools in healthcare.